Spatial Pattern of the Unidirectional Trends in Thermal Bioclimatic Indicators in Iran

Spatiotemporal changes in future precipitation of Afghanistan for shared socioeconomic pathways

Global warming induces spatially heterogeneous changes in precipitation patterns, highlighting the need to assess these changes at regional scales. This assessment is particularly critical for Afghanistan, where agriculture serves as the primary livelihood for the population. New global climate model (GCM) simulations have recently been released for the recently established shared socioeconomic pathways (SSPs). This requires evaluating projected precipitation changes under these new scenarios and subsequent policy updates. This research employed six GCMs from the CMIP6 to project spatial and temporal precipitation changes across Afghanistan under all SSPs, including SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5. The employed GCMs were bias-corrected using the Global Precipitation Climatological Center's (GPCC) monthly gridded precipitation data with a 1.0° spatial resolution. Subsequently, the climate change factor was calculated to assess precipitation changes for both the near future (2020-2059) and the distant future (2060-2099). The bias-corrected projections' multi-model ensemble (MME) revealed increased precipitation across most of Afghanistan for SSPs with higher emissions scenarios. The bias-corrected simulations showed a substantial increase in summer precipitation of around 50%, projected under SSP1-1.9 in the southwestern region, while a decline of over 50% is projected in the northwestern region until 2100. The annual precipitation in the northwest region was projected to increase up to 15% for SSP1-2.6. SSP2-4.5 showed a projected annual precipitation increase of around 20% in the southwestern and certain eastern regions in the far future. Furthermore, a substantial rise of approximately 50% in summer precipitation under SSP3-7.0 is expected in the central and western regions in the far future. However, it is crucial to note that the projected changes exhibit considerable uncertainty among different GCMs.

Projection of bioclimatic patterns via CMIP6 in the Southeast Region of Türkiye: A guidance for adaptation strategies for climate policy

Over the past three decades, global urbanization and climate change have caused significant differences in climate conditions between urban and rural environments. The effects of global warming affect the climatic values in the urban area. The bioclimatic comfort in an area effectively chooses a site regarding the urban quality of life and activities. This study aims to predict the temporal and spatial changes of the bioclimatic comfort zones of Gaziantep province in terms of climate comfort in the context of long-term global scenarios. The future climate simulation maps were produced and analyzed comparing comfort conditions according to Shared Socioeconomic Pathways (SSPs) 245 and 585 scenarios of the Intergovernmental Panel on Climate Change's (IPCC) Coupled Model Intercomparison Project (CMIP) Phase 6 (CMIP6). Spatio-temporal changes in temperature, humidity, and bioclimatic comfort areas were analyzed to inform these efforts according to Thom's discomfort index (DI) and effective temperature-taking wind velocity (ETv). The current situation of bioclimatic comfort areas to examine their synergy under extreme hot weather throughout the province and their possible concerns in 2040, 2060, 2080, and 2100 were modeled using ArcGIS 10.8 software. SSP585/2100 will create hot (84%) areas, according to DI, and warm (29%) areas, according to ETv. The spatial results of the research are discussed, and some strategies are produced in terms of urban planning, design, and engineering.

Thermal bioclimatic indicators over Southeast Asia: present status and future projection using CMIP6

Mapping potential changes in bioclimatic characteristics are critical for planning mitigation goals and climate change adaptation. Assessment of such changes is particularly important for Southeast Asia (SEA) - home to global largest ecological diversity. Twenty-three global climate models (GCMs) of Coupled Model Intercomparison Project Phase 6 (CMIP6) were used in this study to evaluate changes in 11 thermal bioclimatic indicators over SEA for two shared socioeconomic pathways (SSPs), 2-4.5 and 5-8.5. Spatial changes in the ensemble mean, 5th, and 95th percentile of each indicator for near (2020-2059) and far (2060-2099) periods were examined in order to understand temporal changes and associated uncertainty. The results indicated large spatial heterogeneity and temporal variability in projected changes of bioclimatic indicators. A higher change was projected for mainland SEA in the far future and less in maritime region during the near future. At the same time, uncertainty in the projected bioclimatic indices was higher for mainland than maritime SEA. Analysis of mean multi-model ensemble revealed a change in mean temperature ranged from - 0.71 to 3.23 °C in near and from 0.00 to 4.07 °C in far futures. The diurnal temperature range was projected to reduce over most of SEA (ranging from - 1.1 to - 2.0 °C), while isothermality is likely to decrease from - 1.1 to - 4.6%. A decrease in isothermality along with narrowing of seasonality indicated a possible shift in climate, particularly in the north of mainland SEA. Maximum temperature in the warmest month/quarter was projected to increase a little more than the coldest month/quarter and the mean temperature in the driest month to increase more than the wettest month. This would cause an increase in the annual temperature range in the future.

Proposal of a Water-Quality Index for High Andean Basins: Application to the Chumbao River, Andahuaylas, Peru

The water from the high Andean rivers is peculiar due to its composition and the geomorphology of its sources, and naturally or anthropogenically contamination is not discarded along its course. This water is used for agriculture and human consumption, therefore knowing its quality is important. This research aimed to proposing and formulate a water-quality index for high Andean basins through the Delphi method, and its application in the Chumbao River located in Andahuaylas-Peru. Forty-three water-quality parameters were evaluated through the Delphi method, and the water-quality index (WQIHA) was formulated with a weighted average of the weights of the selected parameters, it was compared with the WQI Dinius. For this purpose, ten sampling points were considered along the Chumbao River located between 4274 and 2572 m of altitude and the WQIHA was applied. In addition, field and laboratory analyses were carried out in 2018, 2019, and 2021, in dry and rainy seasons. Twenty parameters were grouped in the physicochemical sub-index (SIPC), heavy metals sub-index (SIHM), and organic matter sub-index (SIOM). Each group contributed with weights of 0.30, 0.30, and 0.40, respectively, for the WQIHA formulation. The SIPC and SIOM showed that the areas near the head of the basin presented excellent and good quality, while the urbanized areas were qualified as marginal to poor; SIHM reported good quality in all points and seasons. Regarding the WQIHA, the index shows good quality in the zones above 3184 m of altitude, contrasting with poor quality downstream, decreasing notably in both seasons, suggesting continuous degradation of the water body.

Selection of the gridded temperature dataset for assessment of thermal bioclimatic environmental changes in Amu Darya River basin

Assessment of the thermal bioclimatic environmental changes is important to understand ongoing climate change implications on agriculture, ecology, and human health. This is particularly important for the climatologically diverse transboundary Amy Darya River basin, a major source of water and livelihood for millions in Central Asia. However, the absence of longer period observed temperature data is a major obstacle for such analysis. This study employed a novel approach by integrating compromise programming and multicriteria group decision-making methods to evaluate the efficiency of four global gridded temperature datasets based on observation data at 44 stations. The performance of the proposed method was evaluated by comparing the results obtained using symmetrical uncertainty, a machine learning similarity assessment method. The most reliable gridded data was used to assess the spatial distribution of global warming-induced unidirectional trends in thermal bioclimatic indicators (TBI) using a modified Mann-Kendall test. Ranking of the products revealed Climate Prediction Center (CPC) temperature as most efficient in reconstruction observed temperature, followed by TerraClimate and Climate Research Unit. The ranking of the product was consistent with that obtained using SU. Assessment of TBI trends using CPC data revealed an increase in the T_min in the coldest month over the whole basin at a rate of 0.03-0.08 °C per decade, except in the east. Besides, an increase in diurnal temperature range and isothermally increased in the east up to 0.2 °C and 0.6% per decade, respectively. The results revealed negative implications of thermal bioclimatic change on water, ecology, and public health in the eastern mountainous region and positive impacts on vegetation in the west and northwest.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s00477-022-02172-8.

Estimation of Groundwater Depletion in Iran’s Catchments Using Well Data

Iran is experiencing significant water challenges that have now turned water security into a national priority. By estimating secular trend groundwater storage in Iran between 2002 and 2017, we see that there is an intensive negative trend, even −4400 Mm3 in some areas. These estimations show shifting in the climate and extra extraction from aquifers for agricultural use in some areas in Iran. The secular trend of groundwater storage changes across the whole of Iran inferred from observation well data is −20.08 GT/yr. The secular trends of GWS changes based on observation well data are: −11.55 GT/yr for the Central Plateau basin, −3.60 GT/yr for the Caspian Sea basin, −3.0 GT/yr for the Persian Gulf and Oman Sea basin, −0.53 GT/yr for the Urmieh Lake basin, −0.57 GT/yr for the Eastern Boundary basin, and −0.83 GT/yr for the Gharaghom basin. The most depleted sub-basin (Kavir Markazi) has secular trends of GWS changes of −4.503 GT/yr. This study suggests that groundwater depletion is the largest single contributor to the observed negative trend of groundwater storage changes in Iran, the majority of which occurred after the drought in 2007. The groundwater loss that has been accrued during the study period is particularly alarming for Iran, which is already facing severe water scarcity.

Analyzing WSTP trend: a new method for global warming assessment

This paper tries to introduce a time-series of temperature parameters as a potential method for studying the global warming. So, we investigated the spatial-temporal variations of warm-season temperature parameters (WSTP), including start time, end time, length of season, base value, peak time, peak value, amplitude, large integrated value, right drive, and left drive, using a database of 30 years' period in different climates of Iran. We used daily temperature data from 1989 to 2018 over Iran to extract the parameters by TIMESAT software. We studied the trend analysis of WSTP through the Mann-Kendall method. Then, we considered the Pearson correlation coefficient to calculate the correlation between WSTP and time. We assessed the trends of the slope using a simple linear regression method. Then, we compared the results of the WSTP trend analysis in climatic zones. Our results accused the hyper-arid climatic zone has the longest warm season (194.89 days a year). The warm season in this region starts earlier than other regions and increases with moderate speed (left drive, 0.19 °C day^-1). Then, it reaches a peak value (31.3 °C) earlier than the different climatic zones. On the other hand, the humid regions' warm season starts with the shortest length and ends later than the other climatic zones (112.1 and 297.5 days a year for start and end times, respectively). We detected that the trend of the start time parameter has decreased by 98.02% of the study area during the last 30 years. The base value, length, and large integrated value parameters have an increasing trend of 66.47%, 80.11%, and 92.95% in Iran. The highest correlation coefficient with time was for start time and large integrated value parameters. Hence, the start time and large integrated value parameters have almost the most negative (< - 0.5) and positive (> 5) trend slope, among other parameters, respectively. In general, these results demonstrate that the studied region has faced global warming impacts over time by increasing the warm season and thermal energy, especially in arid and hyper-arid. We highlight the necessity of planning the land use under the high natural vulnerability of the studied local, especially in this new age of global warming.

Projection of meteorological droughts in Nigeria during growing seasons under climate change scenarios

Like many other African countries, incidence of drought is increasing in Nigeria. In this work, spatiotemporal changes in droughts under different representative concentration pathway (RCP) scenarios were assessed; considering their greatest impacts on life and livelihoods in Nigeria, especially when droughts coincide with the growing seasons. Three entropy-based methods, namely symmetrical uncertainty, gain ratio, and entropy gain were used in a multi-criteria decision-making framework to select the best performing General Circulation Models (GCMs) for the projection of rainfall and temperature. Performance of four widely used bias correction methods was compared to identify a suitable method for correcting bias in GCM projections for the period 2010–2099. A machine learning technique was then used to generate a multi-model ensemble (MME) of the bias-corrected GCM projection for different RCP scenarios. The standardized precipitation evapotranspiration index (SPEI) was subsequently computed to estimate droughts from the MME mean of GCM projected rainfall and temperature to predict possible spatiotemporal changes in meteorological droughts. Finally, trends in the SPEI, temperature and rainfall, and return period of droughts for different growing seasons were estimated using a 50-year moving window, with a 10-year interval, to understand driving factors accountable for future changes in droughts. The analysis revealed that MRI-CGCM3, HadGEM2-ES, CSIRO-Mk3-6-0, and CESM1-CAM5 are the most appropriate GCMs for projecting rainfall and temperature, and the linear scaling (SCL) is the best method for correcting bias. The MME mean of bias-corrected GCM projections revealed an increase in rainfall in the south-south, southwest, and parts of the northwest whilst a decrease in the southeast, northeast, and parts of central Nigeria. In contrast, rise in temperature for entire country during most of the cropping seasons was projected. The results further indicated that increase in temperature would decrease the SPEI across Nigeria, which will make droughts more frequent in most of the country under all the RCPs. However, increase in drought frequency would be less for higher RCPs due to increase in rainfall.

Changes in Climatic Water Availability and Crop Water Demand for Iraq Region

Decreases in climatic water availability (CWA) and increases in crop water demand (CWD) in the background of climate change are a major concern in arid regions because of less water availability and higher irrigation requirements for crop production. Assessment of the spatiotemporal changes in CWA and CWD is important for the adaptation of irrigated agriculture to climate change for such regions. The recent changes in CWA and CWD during growing seasons of major crops have been assessed for Iraq where rapid changes in climate have been noticed in recent decades. Gridded precipitation of the global precipitation climatology center (GPCC) and gridded temperature of the climate research unit (CRU) having a spatial resolution of 0.5°, were used for the estimation of CWA and CWD using simple water balance equations. The Mann–Kendall (MK) test and one of its modified versions which can consider long-term persistence in time series, were used to estimate trends in CWA for the period 1961–2013. In addition, the changes in CWD between early (1961–1990) and late (1984–2013) periods were evaluated using the Wilcoxon rank test. The results revealed a deficit in water in all the seasons in most of the country while a surplus in the northern highlands in all the seasons except summer was observed. A significant reduction in the annual amount of CWA at a rate of −1 to −13 mm/year was observed at 0.5 level of significance in most of Iraq except in the north. Decreasing trends in CWA in spring (−0.4 to −1.8 mm/year), summer (−5.0 to −11 mm/year) and autumn (0.3 to −0.6 mm/year), and almost no change in winter was observed. The CWA during the growing season of summer crop (millet and sorghum) was found to decrease significantly in most of Iraq except in the north. The comparison of CWD revealed an increase in agricultural water needs in the late period (1984–2013) compared to the early period (1961–1990) by 1.0–8.0, 1.0–14, 15–30, 14–27 and 0.0–10 mm for wheat, barley, millet, sorghum and potato, respectively. The highest increase in CWD was found in April, October, June, June and April for wheat, barley, millet, sorghum and potato, respectively.

Spatial Shift of Aridity and Its Impact on Land Use of Syria

Expansion of arid lands due to climate change, particularly in water stressed regions of the world can have severe implications on the economy and people’s livelihoods. The spatiotemporal trends in aridity, the shift of land from lower to higher arid classes and the effect of this shift on different land uses in Syria have been evaluated in this study for the period 1951–2010 using high-resolution monthly climate data of the Terrestrial Hydrology Research Group of Princeton University. The trends in rainfall, temperature and potential evapotranspiration were also evaluated to understand the causes of aridity shifts. The results revealed an expansion of aridity in Syria during 1951–1980 compared to 1981–2010. About 6.21% of semi-arid land was observed to shift to arid class and 5.91% dry-subhumid land to semi-arid land between the two periods. Analysis of results revealed that the decrease in rainfall is the major cause of increasing aridity in Syria. About 28.3% of agriculture land located in the north and the northwest was found to shift from humid to dry-subhumid or dry-subhumid to semi-arid. Analysis of results revealed that the shifting of drylands mostly occurred in the northern agricultural areas of Syria. The land productivity and irrigation needs can be severely affected by increasing aridity which may affect food security and the economy of the country.

Influence of Surface Water Bodies on the Land Surface Temperature of Bangladesh

Recent climate change has resulted in the reduction of several surface water bodies (SWBs) all around the globe. These SWBs, such as streams, rivers, lakes, wetlands, reservoirs, and creeks have a positive impact on the cooling of the surrounding climate and, therefore, reduction in SWBs can contribute to the rise of land surface temperature (LST). This study presents the impact of SWBs on the LST across Bangladesh to quantify their roles in the rapid temperature rise of Bangladesh. The moderate resolution imaging spectroradiometer (MODIS) LST and water mask data of Bangladesh for the period 2000–2015 are used for this purpose. Influences of topography and geography on LST were first removed, and then regression analysis was conducted to quantify the impact of SWBs on the LST. The non-parametric Mann–Kendall (MK) test was used to assess the changes in LST and SWBs. The results revealed that SWBs were reduced from 11,379 km2 in 2000 to 9657 km2 in 2015. The trend analysis showed that changes in SWBs have reduced significantly at a 90% level of confidence, which contributed to the acceleration of LST rise in the country due to global warming. The spatial analysis during the specific years showed that an increase in LST can be seen with the reduction of SWBs. Furthermore, the reduction of 100 m2 of SWBs can reduce the LST of the surrounding regions from −1.2 to −2.2 °C.

Spatio-Temporal Pattern in the Changes in Availability and Sustainability of Water Resources in Afghanistan

Water is gradually becoming scarce in Afghanistan like in many other regions of the globe. The objective of this study was to evaluate the spatial changes in the availability and sustainability of water resources in Afghanistan. The Terrestrial Water Storage (TWS) data of the Gravity Recovery and Climate Experiment (GRACE) satellite obtained from three different institutes, having 1° × 1° spatial resolution for the period 2002–2016 was used for this purpose. Sen’s slope method was used to assess the rate of change, and the Modified Mann–Kendall test was used for the evaluation of the significance of trends in TWS. After, the concept of reliability–resiliency–vulnerability (RRV) was used for assessing the spatial distribution of sustainability in water resources. The results revealed a significant decrease in water availability in the country over the last 15 years. The decrease was found to be highest in the central region where most of the population of the country resides. The reliability in water resources was found high in the northeast Himalayan region and low in the southwest desert; resilience was found low in the central region, while vulnerability was found high in the south and the southeast. Overall, the water resources of the country were found most sustainable in the northeast and southwest and least in the south and the central parts. The maps of water resource sustainability and the changes in water availability produced in the present study can be used for long-term planning of water resources for adaptation to global changes. Besides, those can be used for the management of water resources in a sustainable and judicious manner.

Evaluation of Empirical Reference Evapotranspiration Models Using Compromise Programming: A Case Study of Peninsular Malaysia

Selection of appropriate empirical reference evapotranspiration (ETo) estimation models is very important for the management of agriculture, water resources, and environment. Statistical metrics generally used for performance assessment of empirical ETo models, on a station level, often give contradictory results, which make the ranking of methods a challenging task. Besides, the ranking of ETo estimation methods for a given study area based on the rank at different stations is also a difficult task. Compromise programming and group decision-making methods have been proposed in this study for the ranking of 31 empirical ETo models for Peninsular Malaysia based on four standard statistical metrics. The result revealed the Penman-Monteith as the most suitable method of estimation of ETo, followed by radiation-based Priestley and Taylor and the mass transfer-based Dalton and Meyer methods. Among the temperature-based methods, Ivanov was found the best. The methodology suggested in this study can be adopted in any other region for an easy but robust evaluation of empirical ETo models.

Seasonal Drought Pattern Changes Due to Climate Variability: Case Study in Afghanistan

We assessed the changes in meteorological drought severity and drought return periods during cropping seasons in Afghanistan for the period of 1901 to 2010. The droughts in the country were analyzed using the standardized precipitation evapotranspiration index (SPEI). Global Precipitation Climatology Center rainfall and Climate Research Unit temperature data both at 0.5° resolutions were used for this purpose. Seasonal drought return periods were estimated using the values of the SPEI fitted with the best distribution function. Trends in climatic variables and SPEI were assessed using modified Mann–Kendal trend test, which has the ability to remove the influence of long-term persistence on trend significance. The study revealed increases in drought severity and frequency in Afghanistan over the study period. Temperature, which increased up to 0.14 °C/decade, was the major factor influencing the decreasing trend in the SPEI values in the northwest and southwest of the country during rice- and corn-growing seasons, whereas increasing temperature and decreasing rainfall were the cause of a decrease in SPEI during wheat-growing season. We concluded that temperature plays a more significant role in decreasing the SPEI values and, therefore, more severe droughts in the future are expected due to global warming.