Assessing climate change impacts on water resources and crop yield: a case study of Varamin plain basin, Iran

Assessing environmental sustainability of a vital crop in a critical region: Investigating climate change impacts on agriculture using the SWAT model and HWA method

Drought is an occurrence that brings about significant changes to the structure of areas. Its influence is especially noticeable in important regions with dry and semi-dry weather patterns, leading to a range of difficulties including interruptions in food distribution systems, lack of water, health problems, economic declines, increased migration, and inadequate energy supply. The Ardabil plain, located in Asia and the northern-western region of Iran, plays a pivotal role in crop productions within an arid environment and holds significant political importance for the country. The main objective of this study is to enhance environmental sustainability in this critical and vulnerable region, particularly in anticipation of imminent droughts. The study focuses on examining the financial impacts on agriculture and selection a crop using the SWAT model, HWA method and climate scenarios under the RCP8.5 pathway for the future period (2040-2050). Results for the near future indicate a notable decline in rainfall of around 38 %, a reduction in wheat production by approximately 25 %, and an increase in temperature of around 30 %. At present, the Ardabil Plain produces a total of 284,182 tons of wheat, with 204,980 tons from irrigated crops and 79,202 tons from rain-fed crops. However, the projected future scenario indicates a decrease in total wheat production to 202,926 tons, with 153,855 tons from irrigated crops and 49,071 tons from rain-fed crops. This decline in production is expected to lead to a total net income loss of approximately -$139,372,437, with -$87,690,344 attributed to irrigated crops and -$51,682,092 to rain-fed crops. The comprehensive hierarchy of crop choices yielded by the HWA method is outlined as follows: barley holds a superior position, followed by wheat, soybeans, and potatoes. The study findings suggest that the availability of water sources in certain regions may prompt a shift in farming land from the north to the south of the plain to promote environmental sustainability.

Exploring Dynamics of Water, Energy, and Food Systems in Agricultural Landscapes Using Mental Modeling: A Case of Varamin Plain, Iran

This study applies the mental model and cognitive mapping method to involve stakeholders in delineating the mutual relations between sources of water, energy, and food (WEF) production in the Varamin Plain (VP). Through involving farmers and managerial experts, the approach facilitates the deployment of community communication patterns to recognize and comprehend problems and move from single-loop learning to double-loop learning. The dynamic model was driven from the final mental model of the participants to reflect changes in the systems over time. The system dynamic (SD) model incorporates three scenarios for enhancing irrigation efficiency, managing groundwater extraction, and satisfying environmental needs. The results uncovered that the surface and underground water resources of the VP will gradually decrease within the next two decades in the range of 158 and 2700 million cubic meters (MCM) per year. Also, the plain suffers from water insecurity and a 162 MCM shortage. Consequently, focusing on understanding the nexus and nexus governance can enhance resource management and achieve sustainable development goals. Essentially, promoting collaborative governance, such as creating cooperative organizations and implementing double-loop learning, and instituting a water market, regulatory governance, and monitoring laws can improve the state of Varamin Plain's resources. These results carry important policy implications for using mental models to consider dynamics for discussions on participatory management of the WEF system nexus and environmental management.

The impacts of climate change on hydrological processes of Gilgel Gibe catchment, southwest Ethiopia

Climate change is a significant driver of water resource availability, affecting the magnitude of surface runoff, aquifer recharge, and river flows. This study investigated the impact of climate change on hydrological processes within the Gilgel Gibe catchment and aimed to determine the level of exposure of water resources to these changes, which is essential for future adaptability planning. To achieve this objective, an ensemble mean of six regional climate models (RCMs) from the coordinated regional climate downscaling experiment (CORDEX)-Africa was used to simulate future climatic scenarios. The RCMs outputs were then bias corrected using distribution mapping to match observed precipitation and temperature. The Soil and Water Assessment Tool (SWAT) model was used to assess the hydrological impacts of climate change on the catchment. The results indicated that the ensemble mean of the six RCMs projects a decline in precipitation and an increase in temperature under both the RCP4.5 and RCP8.5 representative concentration pathways. Moreover, the increases in both maximum and minimum temperatures are higher for higher emission scenarios, indicating that RCP8.5 is warmer than RCP4.5. The projected climate change shows a decrease in surface runoff, groundwater, and water yield, resulting in an overall decline of annual flow. This decline is mainly due to the reduction in seasonal flows driven by climate change scenarios. The changes in precipitation range from -11.2% to -14.3% under RCP4.5 and from -9.2% to -10.0% under RCP8.5, while the changes in temperature range from 1.7°C to 2.5°C under RCP4.5 and from 1.8°C to 3.6°C under RCP8.5. These changes could lead to reduced water availability for crop production, which could be a chronic issue for subsistence agriculture. Additionally, the reduction of surface water and groundwater could further exacerbate water stress in the downstream areas, affecting the availability of water resources in the catchment. Furthermore, the increasing demands for water, driven by population growth and socioeconomic progress, along with the variability in temperature and evaporation demands, will amplify prolonged water scarcity. Therefore, robust climate-resilient water management policies are indispensable to manage these risks. In conclusion, this study highlights the importance of considering the impact of climate change on hydrological processes and the need for proactive adaptation measures to mitigate the impacts of climate change on water resources.

Non-stationary response of rain-fed spring wheat yield to future climate change in northern latitudes

The non-stationary response of crop growth to changes in hydro-climatic variables makes yield projection uncertain and the design and implementation of adaptation strategies debatable. This study simulated the time-varying behavior of the underlying cause-and-effect mechanisms affecting spring wheat yield (SWY) under various climate change and nitrogen (N) application scenarios in the Red Deer River basin in agricultural lands of the western Canadian Prairies. A calibrated and validated Soil and Water Assessment Tool and Analysis of Variance decomposition methods were utilized to assess the contribution of crop growth parameters, Global Climate Models, Representative Concentration Pathways, and downscaling techniques to the total SWY variance for the 2040-2064 period. The results showed that the cause-and-effect mechanisms, driving crop yield, shifted from water stress (W-stress) dominated (27 days of W-stress days) during the historical period to nitrogen stress (N-stress) dominated (27 to 35 N-stress days) in the future period. It was shown that while higher precipitation, warmer weather, and early snowmelts, along with elevated CO₂ may favor SWY in cold regions in the future (up to 50% more yields in some sub-basins), the yield potentials may be limited by N-stress (only up to 0.7% yield increase in some sub-basins). The N-stress might be partially related to the N deficiency in the soil, which can be compensated by N fertilizer application. However, inadequate N uptake due to limited evapotranspiration under elevated atmospheric CO₂ might pose restrictions to SWY potentials even in the least N deficient regions. This study uncovers important information on the understanding of spatiotemporal variability of hydrogeochemical processes driving crop yields and the non-stationary response of yields to changing climate. The results also underscore spatiotemporal variability of N-stress due to N deficiency in the soil or N uptake by crops, both of which may restrain SWY by changes in atmospheric CO₂ concentrations in the future.

Assessing the impacts of historical and future land use and climate change on the streamflow and sediment yield of a tropical mountainous river basin in South India

In this study, the impacts of land use/land cover (LULC) and climate change on the streamflow and sediment yield were investigated for the Payaswani River Basin, Western Ghats, India. The LULC was determined using Landsat images, and climate data were procured from five general circulation models for representative concentration pathway (RCP) 4.5 (moderate emission) and 8.5 (high emission). The land change modeler was used to derive the future LULC and its changes from 1988 (historical) to 2030 (future) by using the transition matrix method. The SWAT model was used to assess the impacts of LULC and climate change for the streamflow and sediment yield. The results showed that decrease in forests and grasslands and increase in plantation, agricultural, and urban areas from 1988 to 2030 would lead to an increase in the mean streamflow (11.23%) and sediment yield (17.41%). Under RCP 4.5, climate change would decrease the streamflow by 2.38% in 2030. However, under RCP 8.5, climate change would increase the streamflow by 0.12% in 2030. The sediment yield under RCP 4.5 and 8.5 would increase by 1.23% and 3.33%, respectively. In comparison with the baseline condition, by 2030 future changes in the LULC and climate would increase the streamflow by 7.05% and 11.71% under RCP 4.5 and 8.5, respectively. The sediment yield would increase by 7.92% and 27.11% under RCP 4.5 and 8.5, respectively. The streamflow and sediment yield were predicted to increase in the summer and winter but decrease in the monsoon season.

Spatio-Temporal Analysis of Historical and Future Climate Data in the Texas High Plains

Agricultural production in the Texas High Plains (THP) relies heavily on irrigation and is susceptible to drought due to the declining availability of groundwater and climate change. Therefore, it is meaningful to perform an overview of possible climate change scenarios to provide appropriate strategies for climate change adaptation in the THP. In this study, spatio-temporal variations of climate data were mapped in the THP during 2000–2009, 2050–2059, and 2090–2099 periods using 14 research-grade meteorological stations and 19 bias-corrected General Circulation Models (GCMs) under representative concentration pathway (RCP) scenarios RCP 4.5 and 8.5. Results indicated different bias correction methods were needed for different climatic parameters and study purposes. For example, using high-quality data from the meteorological stations, the linear scaling method was selected to alter the projected precipitation while air temperatures were bias corrected using the quantile mapping method. At the end of the 21st century (2090–2099) under the severe CO2 emission scenario (RCP 8.5), the maximum and minimum air temperatures could increase from 3.9 to 10.0 °C and 2.8 to 8.4 °C across the entire THP, respectively, while precipitation could decrease by ~7.5% relative to the historical (2000–2009) observed data. However, large uncertainties were found according to 19 GCM projections.

A comprehensive review of climate change impacts, adaptation, and mitigation on environmental and natural calamities in Pakistan

The devastations and damages caused by climate change are apparent across the globe, specifically in the South Asian region where vulnerabilities to climate change among residents are high and climate change adaptation and mitigation awareness are extremely low. Pakistan's low adaptive capacity due to high poverty rate, limited financial resources and shortage of physical resources, and continual extreme climatic events including varying temperature, continual flooding, melting glaciers, saturation of lakes, earthquakes, hurricanes, storms, avalanches, droughts, scarcity of water, pest diseases, human healthcare issues, and seasonal and lifestyle changes have persistently threatened the ecosystem, biodiversity, human communities, animal habitations, forests, lands, and oceans with a potential to cause further damages in the future. The likely effect of climate change on common residents of Pakistan with comparison to the world and their per capita impact of climate change are terribly high with local animal species such as lions, vultures, dolphins, and tortoise facing extinction regardless of generating and contributing diminutively to global GHG emissions. The findings of the review suggested that GHG emissions cause climate change which has impacted agriculture livestock and forestry, weather trends and patterns, food water and energy security, and society of Pakistan. This review is a sectorial evaluation of climate change mitigation and adaption approaches in Pakistan in the aforementioned sectors and its economic costs which were identified to be between 7 to 14 billion USD per annum. The research suggested that governmental interference is essential for sustainable development of the country through strict accountability of resources and regulation implemented in the past for generating state-of-the-art climate policy.

Review of climate change impacts on predicted river streamflow in tropical rivers

Tropical regions are characterized by hydrological extreme events, which are likely to be exacerbated by climate change. Therefore, quantifying the extent to which climate change may damage a hydrological system becomes crucial. This paper aims to evaluate the findings from previous research on projected impacts of climate change on hydrological systems located in regions bounded by the Tropic of Cancer and the Tropic of Capricorn. It intends to provide an in-depth understanding of the climatic conditions, applied approaches, climate change impacts on future streamflow, and measures to reduce prediction uncertainty in the tropics. The review revealed that there is a significant variation in the magnitude of climate change impacts on streamflow in the tropics. The reason for the inconsistent trend prediction is that projections are heavily dependent on the trajectory of greenhouse gas emissions, climate model structural differences, and uncertainty of downscaling methods and hydrological models. Therefore, to minimize the uncertainty and maximize confidence in streamflow projections, it is essential to apply multi-member model ensembles and to clarify the adaptation strategy (coping, adjusting, or transforming).

Potential impacts of climate change on groundwater level through hybrid soft-computing methods: a case study-Shabestar Plain, Iran

Groundwater aquifers have always been confronted with significant challenges around the world such as climate change, over-extraction, pollution by wastewaters, and saltwater intrusion in coastal areas. Prediction of groundwater level under the effects of climate change is more important in water resource management. This study has therefore been evaluated the effects of two climate parameters (i.e., precipitation and temperature) in groundwater level for the Shabestar Plain, Iran. For this end, four models from General Circulation Models (GCM) were then used to evaluate future climate change scenarios of the Representative Concentration Pathway (i.e., RCP2.6, RCP4.5, RCP8.5). In the next phase, to reduce the spatial complexity of observation wells, clustering analysis was used. In case of groundwater level modeling, time series in the base period, Least Square Support Vector Machine (LSSVM), Adaptive Neuro-Fuzzy Inference System (ANFIS), and Nonlinear Autoregressive Network with Exogenous inputs (NARX) were also used. To improve the prediction accuracy, time series preprocessing made by wavelet-based de-noising approach was used. Analysis of the results illustrates an increase in temperature and a decrease in precipitation for study region in the future period times. The results also reveal that hybrid techniques of the wavelet-NARX give best results in comparison with the other models. A simulation result illustrates that the groundwater level declines in RCP2.6, 4.5, and 8.5, which gives average levels of 0.61, 0.81, and 1.53 m, respectively, for the future period years (i.e., 2020-2024). These results would lead to continuous groundwater depletion. These findings emphasize the necessity of the importance of extraction policies in water resource management.