Tag: Climate Change Downscaling Techniques

Developing climate change adaptation pathways in the agricultural sector based on robust decision-making approach (case study: Sefidroud Irrigation Network, Iran)

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Abstract

Allocation of water in the situation of climate change presents various uncertainties. Consequently, decisions must be made to ensure stability and functionality across different climatic scenarios. This study aims to examine the effectiveness of adaptation strategies in the agricultural sector, including a 5% increase in irrigation efficiency (S1) and a shift in irrigation method to Dry-DSR (direct seeded rice) under conditions of climatic uncertainty using a decision-making approach. The study focuses on the basin downstream of the Sefidroud dam, encompassing the Sefidroud irrigation and drainage network. Initially, basin modeling was conducted using the WEAP integrated management software for the period 2006–2020. Subsequently, the impact of climate change was assessed, considering RCP2.6, RCP4.5, and RCP8.5 emission scenarios on surface water resources from 2021 to 2050. Runoff and cultivated area, both subject to uncertainty, were identified as key parameters. To evaluate strategy performance under different uncertainties and determine the efficacy of each strategy, regret and satisfaction approaches were employed. Results indicate a projected decrease in future rainfall by 3.5–11.8% compared to the base period, accompanied by an increase in maximum and minimum temperatures (0.83–1.62 °C and 1.15–1.33 °C, respectively). Inflow to the Sefidroud dam is expected to decrease by 13–28%. Presently, the Sefidroud irrigation and drainage network faces an annual deficit of 505.4 MCM, and if current trends persist with the impact of climate change, this shortfall may increase to 932.7 MCM annually. Furthermore, satisfaction indices for strategy (S2) are 0.77 in an optimistic scenario and 0.70 in strategy (S1). In a pessimistic scenario, these indices are 0.67 and 0.56, respectively. Notably, changing the irrigation method with Dry-DSR is recommended as a robust strategy, demonstrating the ability to maintain basin stability under a broad range of uncertainties and climate change scenarios. It is crucial to note that the results solely highlight the effects of climate change on water sources entering the Sefidroud dam. Considering anthropogenic activities upstream of the Sefidroud basin, water resource shortages are expected to increase. Therefore, reallocating water resources and implementing practical and appropriate measures in this area are imperative.

Assessment of historical and projected changes in extreme temperatures of Balochistan, Pakistan using extreme value theory

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Abstract

The fundamental consequences of global warming include an upsurge in the intensity and frequency of temperature extremes. This study provides an insight into historical trends and projected changes in extreme temperatures on annual and seasonal scales across “Balochistan, Pakistan”. Historical trends are analyzed through the Mann Kendal test, and extreme temperatures (Tmax and Tmin) are evaluated using generalized extreme value (GEV) distribution for historical period (1991–2020) from the observational data and the two projected periods as near-future (2041–2070) and far-future (2071–2100) using a six-member bias-corrected ensemble of regional climate models (RCMs) projections from the coordinate regional downscaling experiment (CORDEX) based on the worst emission scenario (RCP8.5). The evaluation of historical temperature trends suggests that Tmax generally increase on yearly scale and give mixed signals on seasonal scale (winter, spring, summer, and autumn); however, Tmin trends gave mixed signals at both yearly and seasonal scale. Compared to the historical period, the return levels are generally expected to be higher for Tmax and Tmin during the both projection periods in the order as far-future > near-future > historical on yearly and seasonal basis; however, the changes in Tmin are more evident. Station-averaged anomalies of + 1.9 °C and + 3.6 °C were estimated in 100-year return levels for yearly Tmax for near-future and far-future, respectively, while the anomalies in Tmin were found to be + 3.5 °C and + 4.8 °C which suggest the intensified heatwaves but milder colder extreme in future. The findings provide guidance on improved quantification of changing frequencies and severity in temperature extremes and the associated impacts.

An exhaustive investigation of changes in projected extreme precipitation indices and streamflow using CMIP6 climate models: A case study

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Abstract

This study draws attention to the better comprehension of spatio-temporal analysis of climate changes based on precipitation extremes and projection of future streamflow for efficient management of water resources in the Krishna River Basin (KRB), India. The concept of symmetric uncertainty (SU) is employed to select the top five Global Climate Models (GCMs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6) to project future precipitation extreme indices under different Shared Socio-economic Pathways (SSPs). Grid-wise trend analysis reveals that there is more number of decreasing trends in extreme precipitation indices than increasing trends. From the results, it is observed that the percentage contributions of maximum one-day (RX1day) and five-day (RX5day) precipitation indices to the annual total precipitation indices are more important. In future periods, the precipitation extremes are expected to increase, especially the heavy precipitation indices such as R95p, R99p, RX1day, and RX5day, which are increasing significantly along with R50. The projection of future streamflow in the KRB is done using a Support Vector Machine (SVM) and is expected to increase under different SSPs. These precipitation extremes may increase the chance of hydrological calamities across the basin in the future.

Research highlights

  • Spatio-temporal analysis of extreme precipitation indices is carried out using CMIP6 climate model simulations over KRB.

  • One of the most efficient algorithm, symmetric uncertainty is employed to select best-performing GCMs to reduce the uncertainty in GCM selection.

  • The association between the extreme indices and discharge is carried out using Pearson correlation.

  • A significant increase is observed in projected extreme indices, especially very extreme indices such as 95th and 99th percentiles, RX1day and RX5day.

  • SVM regression is established between TOTPR and mean daily discharge to predict the future annual average streamflow under different SSP scenarios.

Quantifying the spatiotemporal patterns and environmental impacts of surface coal mining in the Xilingol Steppe, Inner Mongolia

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Abstract

Surface coal mining is one of the most environmentally destructive human disturbances. This study provides the first comprehensive assessment of the speed and scale of coal mining and its major environmental impacts in the Xilingol Steppe, Inner Mongolia from 1990 to 2015, using remote sensing data and landscape metrics. Our results show that during this period the number of surface coal mining areas (SCMAs) increased from 40 to 504 (about 13 times), while the size of SCMAs increased from 3.21 km2 to 283.62 km2 (greater than 88 times). The rapid expansion of SCMAs greatly fragmented the steppe landscape, consumed huge amounts of water, damaged rivers and wetlands, and substantially reduced grassland productivity. We estimated that the amount of water consumed by coal mining increased from 2.35 million m3 in 1990 to 242.61 million m3 in 2015 (more than 103 times), negatively affecting all six major rivers and most wetlands in the region. About 222 km2 of steppes were eradicated, resulting in a grassland production loss of 7.17×1010 g C. Our findings indicate that surface coal mining has transformed the steppe landscape and devastated its ecosystem function and services, posing a major threat to the environment of the region. Future studies need to focus on more in-depth integrative assessments of environmental, economic, and social impacts of surface coal mining to seek sustainability solutions for the region.

Statistical downscaling of GCMs wind speed data for trend analysis of future scenarios: a case study in the Lombardy region

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Abstract

Near-surface wind speed is a key climatic variable, affecting many sectors, such as energy production, air pollution, and natural hazard. Lombardy region of Italy is among the European areas with lowest average wind speed, leading generally to low air quality and wind energy potential. However, it is also one of the most affected area by tornadoes in Italy. Here we investigate possible changes in wind circulation as due to prospective global warming. We analysed wind speed WS under future scenarios (SSP1-2.6 and SSP5-8.5) from six Global Climate Models (GCMs) until 2100, tuned against observed WS data. We employed a statistical downscaling method, namely Stochastic Time Random Cascade (STRC) to correct locally GCMs outputs. Three statistical tests, i.e. Linear Regression, Mann Kendall, Moving Window Average, were carried out to analyse future trends of: annual WS averages, 95th quantile (as an indicator of large WS), and the number of days of calm wind per year (NWC). The proposed STRC algorithm can successfully adjust the mean, standard deviation, and autocorrelation structure of the GCM outputs. No strong trends are found for the future. The chosen variables would all display non-stationarity, and the 95th percentile display a positive trend for most of the stations. Concerning NWC, notable discrepancies among GCMs are seen. The STRC algorithm can be used to successfully adjust GCMs outputs to reflect locally observed data and to then generate credible long-term scenarios for WSs as a tool for decision-making.

Prediction of groundwater level using GMDH artificial neural network based on climate change scenarios

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Abstract

One of the main challenges regarding the prediction of groundwater resource changes is the climate change phenomenon and its impacts on quantitative variations of such resources. Groundwater resources are treated as one of the main strategic resources of any region. Given the climate change phenomenon and its impacts on hydrological parameters, it is necessary to evaluate and predict future changes to achieve an appropriate plan to maintain and preserve water resources. In this regard, the present study is put forward by utilizing the Statistical Down-Scaling Model (SDSM) to forecast the main climate variables (i.e., temperature and precipitation) based on new Rcp scenarios for greenhouse gas emissions within a period from 2020 to 2060. The results obtained from the prediction of climate parameters indicate different values in each emission scenario, so the limit, minimum and maximum values occur in the Rcp8.5, Rcp2.6 and Rcp4.5 scenarios, respectively. Also, a model is developed by utilizing the GMDH artificial neural network technique. The developed model predicts the average groundwater level based on the climate variables in such a way that by implementing the climate parameters forecasted by the SDSM model, the groundwater level within a time period from 2020 to 2060 is predicted. The results obtained from the verification and validation of the model imply its proper performance and reasonable accuracy in predicating groundwater level based on the climate variables. The findings derived from the present paper indicate that compared to the years prior to the prediction period, the groundwater level of the Sahneh Plain has dramatically dropped so that based on the Rcp scenarios, the groundwater level values are in their lowest state within the period from 2046 to 2056. The findings of this paper can be used by managers and decision makers as a layout for evaluating climate change effects in the Sahneh Plain.

Assessment of impact of climate change on streamflow and soil moisture in Pare watershed of Arunachal Pradesh, India

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Abstract

A fragile terrain makes a mountainous watershed like Pare in India’s eastern Himalayas extremely sensitive to climate change. However, this watershed has a great deal of potential for the development of water resources; thus, hydrological investigation and impact assessments in light of climate change are essential. Using the soil and water assessment tool (SWAT), the current study examined how discharge and soil moisture in the Pare River of Arunachal Pradesh would change in response to climate change. The projected streamflow was compared with the baseline projection during 1976–2005. The future precipitation scenarios predicted that there will be a decrease in rainy episodes and an increase in dry days in the Pare watershed. This meant that more extreme occurrences would occur in the future. The SWAT model’s performances during calibration and validation were deemed satisfactory based on the Nash–Sutcliffe efficiency, p-factor, and r-factor. It was discovered that SWAT slightly underestimated the discharge. The findings revealed that discharge would continue to rise as time progressed from the near to the far-future. Variations in discharge showed shrinkage in high flow days with increased flood amplitude, which might result in major flooding in low-lying areas downstream and significant soil erosion from the upland areas. It was predicted that the surface runoff component would increase significantly in the future, possibly leading to frequent flash floods and soil erosion. However, soil moisture in the Pare watershed would remain more or less the same throughout this century. Even if future streamflow was predicted to rise, worry would always persist due to its unequal distribution. The region's water managers may set guidelines for water and soil conservation measures to cope up with these changes. More studies may be conducted to recommend actions in the study region by local authorities and managers associated with various soil and water sectors.

Assessment of impact of climate change on streamflow and soil moisture in Pare watershed of Arunachal Pradesh, India

Latest Results

Abstract

A fragile terrain makes a mountainous watershed like Pare in India’s eastern Himalayas extremely sensitive to climate change. However, this watershed has a great deal of potential for the development of water resources; thus, hydrological investigation and impact assessments in light of climate change are essential. Using the soil and water assessment tool (SWAT), the current study examined how discharge and soil moisture in the Pare River of Arunachal Pradesh would change in response to climate change. The projected streamflow was compared with the baseline projection during 1976–2005. The future precipitation scenarios predicted that there will be a decrease in rainy episodes and an increase in dry days in the Pare watershed. This meant that more extreme occurrences would occur in the future. The SWAT model’s performances during calibration and validation were deemed satisfactory based on the Nash–Sutcliffe efficiency, p-factor, and r-factor. It was discovered that SWAT slightly underestimated the discharge. The findings revealed that discharge would continue to rise as time progressed from the near to the far-future. Variations in discharge showed shrinkage in high flow days with increased flood amplitude, which might result in major flooding in low-lying areas downstream and significant soil erosion from the upland areas. It was predicted that the surface runoff component would increase significantly in the future, possibly leading to frequent flash floods and soil erosion. However, soil moisture in the Pare watershed would remain more or less the same throughout this century. Even if future streamflow was predicted to rise, worry would always persist due to its unequal distribution. The region's water managers may set guidelines for water and soil conservation measures to cope up with these changes. More studies may be conducted to recommend actions in the study region by local authorities and managers associated with various soil and water sectors.

Assessment of groundwater level using satellite-based hydrological parameters in North-West India: A deep learning approach

Latest Results

Abstract

Groundwater (GW) has been prominent source of freshwater for sustainable growth of agriculture, water management and urban/industrial purposes. The overexploitation of the water source leads to large variation of groundwater level (GWL) in the Indian sub-continent. The GWL mostly fluctuates via several factors like; groundwater extraction, precipitation, soil moisture, evaporation, etc., and prediction of GWL by collecting these factor for large geographical region is a challenging task. In the study, deep learning (DL) approach, namely Convolution Neural Network-Long short term memory (ConvLSTM) model has been implemented for prediction of the GWL. The model is designed based on the U-Net framework with the integration LSTM unit, to process the spatiotemporal information induced by the GWL factors between the years 2005–2017. The assessment of the GW in North-West India (NWI) has been carried out using several aforementioned hydrological parameters, selected based on correlation. In addition, in-situ groundwater has been used to get GW fluctuation scenarios (i.e., categorised into four cycles PrePre, PrePost, PostPre, and PostPost) w.r.t monsoon season to predict the difference (Δh) in GWL. The proposed model has been tested w.r.t Artificial neural network (ANN) and Convolution neural network (CNN) and cross-validate using several geo-locations information of NWI. The ConvLSTM has outperformed based on overall root means square (RMSE) error of 0.1099, 0.1082, 0.1005 and 0.0957 for each cycle i.e. PrePre, PrePost, PostPre, and PostPost, respectively, compared to ANN and CNN.

Systematic review of the uncertainty of coral reef futures under climate change

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Abstract

Climate change impact syntheses, such as those by the Intergovernmental Panel on Climate Change, consistently assert that limiting global warming to 1.5 °C is unlikely to safeguard most of the world’s coral reefs. This prognosis is primarily based on a small subset of available models that apply similar ‘excess heat’ threshold methodologies. Our systematic review of 79 articles projecting coral reef responses to climate change revealed five main methods. ‘Excess heat’ models constituted one third (32%) of all studies but attracted a disproportionate share (68%) of citations in the field. Most methods relied on deterministic cause-and-effect rules rather than probabilistic relationships, impeding the field’s ability to estimate uncertainty. To synthesize the available projections, we aimed to identify models with comparable outputs. However, divergent choices in model outputs and scenarios limited the analysis to a fraction of available studies. We found substantial discrepancies in the projected impacts, indicating that the subset of articles serving as a basis for climate change syntheses may project more severe consequences than other studies and methodologies. Drawing on insights from other fields, we propose methods to incorporate uncertainty into deterministic modeling approaches and propose a multi-model ensemble approach to generating probabilistic projections for coral reef futures.