Multiple General Circulation Models Research Articles

Climate variability impacts on runoff projection in the 21st century based on the applicability assessment of multiple GCMs: A case study of the Lushi Basin, China

In this study, we initiatively employed the improved Secure Hash Algorithm (SHA) to evaluate and compare the competencies of multiple Coupled Model Intercomparison Project Phase Six (CMIP6) General Circulation Models (GCMs). The selected GCM outputs were bias-corrected by the non-parametric quantile mapping (QM) method. The calibrated and validated data were then used to run the XAJ (Xin’anjiang) model to assess long-term climate variation impacts on runoff under four Shared Socioeconomic Pathways (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5), where the results were compared with the baseline period (1976–2000). Overall, the CNRM-CM6-1-HR, FIO-ESM-2–0, INM-CM5-0 and NorESM2-MM models reconstructed better spatio-temporal evolutionary trends related to historical precipitation and evaporation, and were applied as selected GCMs for reliable runoff projection applications. The intensification amplitudes of precipitation and evaporation produced by selected GCMs gradually spread from SSP1-2.6 to SSP5-8.5 throughout the 21st century. Compared to the baseline period (257 m3/s), the extreme streamflow during the wet periods is likely to increase for SSP5-8.5 (288 ∼ 298 m3/s), SSP3-7.0 (276 ∼ 294 m3/s), and SSP2-4.5 (222 ∼ 280 m3/s), whereas SSP1-2.6 decreases to varying degrees (178 ∼ 255 m3/s). Meanwhile, during the dry periods, the extreme streamflow is projected to decrease by 28 ∼ 73 m3/s (SSP5-8.5), 32 ∼ 38 m3/s (SSP3-7.0), 8 ∼ 31 m3/s (SSP2-4.5), and 5 ∼ 33 m3/s (SSP1-2.6) from historical levels (62 m3/s). The overwhelming majority of increasing trends in the wet periods and vastly decreasing trends in the dry seasons indicate that the basin will face some floods and water shortage risks in the mid to late 21st century. The findings broaden the efficacious applications of SHA in the performance selection of GCMs and QM method in bias correction. The conclusions provide a reference for flood and drought mitigation planning and water resources policymaking in the Yellow River Basin.

Inter‐Comparison of Precipitation Simulation and Future Projections Over China From an Ensemble of Multi‐GCM Driven RCM Simulations

AbstractAn analysis is presented of the precipitation bias and change signal in an ensemble of regional climate model (RCM) (RegCM4) projections driven by multiple general circulation models (GCMs) over China. RegCM4 is driven by five different GCMs for the 120‐year period 1979–2099 at 25 km grid spacing, under the representative concentration pathway RCP8.5. We find that the GCMs and RegCM4 reproduce the general spatial pattern of precipitation over China in all four seasons, with RegCM4 providing greater spatial detail, especially over areas with complex terrain. The spatial patterns of precipitation bias show common features between the GCMs and RegCM4, characterized by an underestimation in the wetter regions, and an overestimation in the drier ones. Systematic increases of precipitation are projected in northern China, most pronounced in the Northwest basins, by both the GCMs and RegCM4 in all seasons except summer, when more mixed results are found. In addition, weak correlations of the projected change patterns are found in summer between the GCMs and nested RegCM4, indicating the greater role played by the representation of local convection processes during this monsoon season. The projections across the RegCM4 experiments show higher consistency and lower spread compared to the GCM ensemble, again indicating that the nested model physics significantly modulates the change signal deriving from the GCM boundary forcing.

Regional event-based flood quantile estimation method for large climate projection ensembles

Emerging large ensemble climate datasets produced by multiple general circulation models and their downscaling products challenge the limits of hydrodynamic models because of the immense data size. To overcome this new challenge and estimate the discharge quantiles corresponding to different return periods at all river sections in an entire region, this study proposes an event-based regional approach that uses a nationwide distributed rainfall–runoff model as well as large climate projection ensembles. This approach addresses the high computational burden associated with continuous simulations and solves the problem of conventional event-based simulations serving only a single outlet of a basin. For our analysis, we extracted 372 annual maximum 48 h rainfall events that cover the entirety of Shikoku Island and its eight major river basins. Peak discharges were estimated using a 150 m resolution rainfall–runoff–inundation model. These discharges were then screened using either the peak-over-threshold (POT) method or block maxima (BM) method, and frequency curves were subsequently constructed and evaluated. The primary reason for the necessity of POT or BM was to avoid interference from extraneous low discharges. The POT-based frequency curves showed good accuracy when using peak discharges in the range of the top 10–50%, and the results remain stable within this threshold range. The BM method, employing block sizes of 2–5 years, can generate relatively accurate frequency curves, but the choice of block size introduces significant variations in results among certain basins. Generally, the accuracy of results based on the POT method surpasses that of the BM method. Considering the accuracy, computational cost, and result stability, the POT method is preferred. The error introduced by the regional approach was acceptable with more than half of the relative root-mean-square errors remaining within 10% and basically all of the results are within 20%. The results of the regional approach exhibited good accuracy across climate scenarios and provided consistent information regarding future flood quantiles. This study serves as the foundation for high-resolution future flood risk assessment.

Do Derived Drought Indices Better Characterize Future Drought Change?

AbstractCurrent methods for climate change assessment ignore the significant differences in uncertainty in model projections of the two key constituents of drought, precipitation, and evapotranspiration. We present here a new basis for assessing future drought using climate model simulations that addresses this limitation. The new method estimates the Standardized Precipitation Evapotranspiration Index (SPEI) in a two‐stage process. The first stage of our proposed approach is to derive the Standardized Precipitation Index (SPI) using reliable atmospheric variables, which are filtered with a wavelet‐based spectral transformation. This derived SPI is then converted to an equivalent SPEI by combining it with climate model evapotranspiration simulations. We assess the performance of our proposed approach across Australia. The consistency of general circulation model (GCM) drought projections, in terms of both frequency and severity, is improved using the derived SPI. Incorporating evapotranspiration further improves the consistency of the multiple GCMs and drought time scales. The proposed framework can also be generalized to other water resources applications, where the differences in GCM uncertainty for precipitation and evapotranspiration affect climate change impact assessments.

Productivity of Soybean under Projected Climate Change in a Semi-Arid Region of West Africa: Sensitivity of Current Production System

The production of soybean is gaining more attention in West Africa. In light of projected changes in climate, there is a need to assess the potential impacts on yield productivity and variability among farmers. An evaluated GROPGRO module of the Decision Support System for Agro-technological Transfer (DSSAT) was used to simulate soybean productivity under both historical (1980–2009) and projected climate scenarios from multiple general circulation models (GCMs) under two representative concentration pathways (RCPs): 4.5 and 8.5. Agronomic data from 90 farms, as well as multiple soil profile data, were also used for the impact assessment. Climate change leads to a reduction (3% to 13.5% across GCMs and RCPs) in the productivity of soybean in Northern Ghana. However, elevated atmospheric carbon dioxide has the potential to offset the negative impact, resulting in increased (14.8% to 31.3% across GCMs and RCPs) productivity. The impact of climate change on yield varied widely amongst farms (with relative standard deviation (RSD) ranging between 17% and 35%) and across years (RSD of between 10% and 15%). Diversity in management practices, as well as differences in soils, explained the heterogeneity in impact among farms. Variability among farms was higher than that among years. The strategic management of cultural practices provides an option to enhance the resilience of soybean productivity among smallholders.

Effects of global climate change on the hydrological cycle and crop growth under heavily irrigated management – A comparison between CMIP5 and CMIP6

Quantifying the impact of global climate change on the water cycle and crop production is essential for water resource management and agricultural production planning. This study compared multiple GCM (General Circulation Model) projections of CMIP5 (Coupled Model Intercomparison Project 5) and CMIP6 and further used the selected GCMs to quantify the impact of future climate change on hydrology and crop production under intensive irrigation management in the North China Plain using an improved SWAT model (SWAT-MAD). Taylor’s skill score was used firstly to screen out six groups of GCMs with better simulation performance from 10 pairs of homologous GCMs of CMIP5 and CMIP6. The selected GCMs of CMIP5 and CMIP6 were further used to drive SWAT-MAD for a robust evaluation of climate change impacts. Results showed that during winter wheat growing season, average actual evapotranspiration (ETa) increased by 3%, 3%, 4%, and 5%, respectively, under 2041–2070 RCP4.5, 2041–2070 SSP2-4.5, 2041–2100 RCP4.5, and 2071–2100 SSP2-4.5 scenarios, compared to the historical period (1971–2000). During summer maize growing season, those changes in ETa were 4%, 2%, −0.2%, and −3%. Predicted future precipitation, air temperatures, and surface runoff could increase, while irrigation could decrease as precipitation increased. The dynamic patterns of leaf area index of winter wheat and summer maize indicated that there is a tendency for early emergence and maturity of both crops in the future, and the daily total biomass elevated with a corresponding increase in final yields. The highest increases in yields of winter wheat and summer maize were 18.9% and 16.7%. The findings not only contribute to enhancing the confidence of future projections using CMIP6 but also facilitate our understanding of the relative uncertainty of GCMs. This study provides technical and data support for pre-selections of GCMs and decision making in best management practices for groundwater conservation and agricultural production.

Investigating Impacts of Climate Change on Runoff from the Qinhuai River by Using the SWAT Model and CMIP6 Scenarios

This paper looks at regional water security in eastern China in the context of global climate change. The response of runoff to climate change in the Qinhuai River Basin, a typical river in eastern China, was quantitatively investigated by using the Soil and Water Assessment Tool (SWAT) model and the ensemble projection of multiple general circulation models (GCMs) under three different shared socioeconomic pathways (SSPs) emission scenarios. The results show that the calibrated SWAT model is applicable to the Qinhuai River Basin and can accurately characterize the runoff process at daily and monthly scales with the Nash–Sutcliffe efficiency coefficients (NSE), correlation coefficients (R), and the Kling–Gupta efficiency (KGE) in calibration and validation periods being above 0.75 and relative errors (RE) are ±3.5%. In comparison to the baseline of 1980–2015, the mean annual precipitation in the future period (2025–2060) under the three emission scenarios of SSP1-2.6, SSP2-4.5, and SSP5-8.5 will probably increase by 5.64%, 2.60%, and 6.68% respectively. Correspondingly, the multiple-year average of daily maximum and minimum air temperatures are projected to rise by 1.6–2.1 °C and 1.4–2.0 °C, respectively, in 2025–2060. As a result of climate change, the average annual runoff will increase by 16.24%, 8.84%, and 17.96%, respectively, in the period of 2025–2060 under the three SSPs scenarios. The increase in runoff in the future will provide sufficient water supply to support socioeconomic development. However, increases in both rainfall and runoff also imply an increased risk of flooding due to climate change. Therefore, the impact of climate change on flooding in the Qinhuai River Basin should be fully considered in the planning of flood control and the basin’s development.

A comparative assessment of CMIP5 and CMIP6 in hydrological responses of the Yellow River Basin, China

Abstract Investigation of the role of multiple general circulation model (GCM) ensembles in obtaining comprehensive knowledge of hydrological responses across the Yellow River Basin (YRB), China, is still of substantial importance. This study evaluates the performance of the Coupled Model Intercomparison Project Phase 6 (CMIP6) models in simulating the hydrological regime in the YRB and compares the results with those from CMIP 5 (CMIP5). The comparison is performed between 21 GCMs from CMIP6 under three Shared Socioeconomic Pathway scenarios and 18 GCMs from CMIP5 under three Representative Concentration Pathway scenarios. Raw CMIP outputs are first corrected and downscaled by the Bias Correction and Spatial Disaggregation methods, and the bias-corrected GCM outputs are then employed to drive the Soil and Water Assessment Tool hydrological model and project streamflow. After correction and downscaling, areal averages for future changes (relative to 1971–2000) of temperature and precipitation are found larger in CMIP6 than in CMIP5. The emblematic annual mean temperature of CMIP6 increases by 1.64–2.20 and 2.31–5.29 °C for the future period of 2026–2055 and 2066–2095, while the counterpart of CMIP5 is 1.92–2.39 and 1.68–4.76 °C, respectively. In terms of precipitation, for CMIP6, it increases by 3.45–4.70 and 6.77–15.40%, and for CMIP5 by 2.58–2.96 and 3.83–9.95%. It is further concluded that: (1) future streamflow will probably decrease less under CMIP6 than that under CMIP5 in most cases, and climate changes of this kind will affect regional water supply and security in the YRB; (2) uncertainty in the projected streamflow is dominated by GCMs uncertainty with the contribution rate of >75%; (3) the streamflow is more sensitive to precipitation changes in comparison with temperature changes in the near future. In contrast, streamflow reduction is more attributed to an increase in temperature with a contribution rate of almost >60% than in precipitation in the far future.

Simulating the potential effects of elevated CO2 concentration and temperature coupled with storm intensification on crop yield, surface runoff, and soil loss based on 25 GCMs ensemble: A site-specific case study in Oklahoma

Proper simulation of storm intensification is critical in projecting crop yield, surface runoff, and soil loss under climate change conditions. We developed a total of 100 climate scenarios coupled with storm intensification, which were based on 25 downscaled GCMs (General Circulation Model) projections under RCP4.5 and RCP8.5 (Representative Concentration Pathways) for two time periods of 2021–2050 and 2051–2080. The climate files were applied to a modified WEPP (Water Erosion Prediction Project) model to estimate crop yield, runoff, and soil loss under 29 combinations of cropping and tillage systems. The results showed that future extreme storm events in the study area would significantly increase during 2021–2080 (P < 0.01). However, average monthly precipitation in summer would decrease by 12.5% in June and 10.8% in July, along with an annual precipitation decline of 2.6%, leading to a decrease in crop yield in this rainfed agricultural region. The amount of runoff (soil loss) from extreme storms would account for 45.9 (64.3%) of the total annual runoff (soil loss) when averaged across two time periods and two RCPs. The average annual runoff depth (soil loss amount) from crop rotation or double cropping systems would be 27.6 (78.1%) less than the corresponding values in a continuous monoculture cropping system. No-till and crop rotations with alfalfa are the best agricultural management alternatives to mitigate soil erosion rates under future climate change. The analysis of variance (ANOVA) indicated that the uncertainty contributions of GCMs and cropping and tillage systems reached 45.5% and 30.4% in annual surface runoff prediction (P < 0.001), while cropping and tillage systems (71.7%, P < 0.001) was the major uncertainty source in annual soil loss simulations. This study improved the prediction of crop yield, runoff, and soil loss from various cropping and tillage systems under climate change conditions by integrating storm intensification from multiple GCM ensembles.

Improving Seasonal Forecast Using Probabilistic Deep Learning

AbstractThe path toward realizing the potential of seasonal forecasting and its socioeconomic benefits relies on improving general circulation model (GCM) based dynamical forecast systems. To improve dynamical seasonal forecasts, it is crucial to set up forecast benchmarks, and clarify forecast limitations posed by model initialization errors, formulation deficiencies, and internal climate variability. With huge costs in generating large forecast ensembles, and limited observations for forecast verification, the seasonal forecast benchmarking and diagnosing task proves challenging. Here, we develop a probabilistic deep learning‐based statistical forecast methodology, drawing on a wealth of climate simulations to enhance seasonal forecast capability and forecast diagnosis. By explicitly modeling the internal climate variability and GCM formulation differences, the proposed Conditional Generative Forecasting (CGF) methodology enables bypassing crucial barriers in dynamical forecast, and offers a top‐down viewpoint to examine how complicated GCMs encode the seasonal predictability information. We apply the CGF methodology for global seasonal forecast of precipitation and 2 m air temperature, based on a unique data set consisting 52,201 years of climate simulation. Results show that the CGF methodology can faithfully represent the seasonal predictability information encoded in GCMs. We successfully apply this learned relationship in real‐world seasonal forecast, achieving competitive performance compared to dynamical forecasts. Using this CGF as benchmark, we reveal the impact of insufficient forecast spread sampling that limits the skill of the considered dynamical forecast system. Finally, we introduce different strategies for composing ensembles using the CGF methodology, highlighting the potential for leveraging the strengths of multiple GCMs to achieve advantgeous seasonal forecast.

Biogeographic consequences of shifting climate for the western massasauga (Sistrurus tergeminus).

The western massasauga (Sistrurus tergeminus) is a small pit viper with an extensive geographic range, yet observations of this species are relatively rare. They persist in patchy and isolated populations, threatened by habitat destruction and fragmentation, mortality from vehicle collisions, and deliberate extermination. Changing climates may pose an additional stressor on the survival of isolated populations. Here, we evaluate historic, modern, and future geographic projections of suitable climate for S. tergeminus to outline shifts in their potential geographic distribution and inform current and future management. We used maximum entropy modeling to build multiple models of the potential geographic distribution of S. tergeminus. We evaluated the influence of five key decisions made during the modeling process on the resulting geographic projections of the potential distribution, allowing us to identify areas of model robustness and uncertainty. We evaluated models with the area under the receiver operating curve and true skill statistic. We retained 16 models to project both in the past and future multiple general circulation models. At the last glacial maximum, the potential geographic distribution associated with S. tergeminus occurrences had a stronghold in the southern part of its current range and extended further south into Mexico, but by the mid‐Holocene, its modeled potential distribution was similar to its present‐day potential distribution. Under future model projections, the potential distribution of S. tergeminus moves north, with the strongest northward trends predicted under a climate scenario increase of 8.5 W/m2. Some southern populations of S. tergeminus have likely already been extirpated and will continue to be threatened by shifting availability of suitable climate, as they are already under threat from desertification of grasslands. Land use and habitat loss at the northern edge of the species range are likely to make it challenging for this species to track suitable climates northward over time.

Potential impacts of climate change on an aquifer in the arid Altiplano, northern Chile: The case of the protected wetlands of the Salar del Huasco basin

Study regionThe Salar del Huasco is an undeveloped basin in the arid Chilean Altiplano. Study focusThis study aims to investigate potential impacts of climate change on the aquifer of the Salar del Huasco basin to better manage groundwater resources in the Chilean Altiplano. With this purpose, precipitation and temperature projections from multiple general circulation models were statistically downscaled using the hybrid delta approach to produce ensemble scenarios. Groundwater recharge was estimated for each scenario based on a rainfall-runoff model that was used to drive a numerical groundwater model, whose results were used to evaluate the future behavior of the aquifer under each scenario from 2020 to 2100. New hydrological insights for the study regionThis study found that higher precipitation would contribute to larger groundwater recharge rates. Additionally, results showed that the aquifer is able to withstand the impacts of climate change through the balance of its water components, maintaining thus stable levels in areas of ecological value. Since the study site is one of the few remaining undeveloped groundwater basins, the results obtained represent the natural response of the system to climate change without interference from anthropogenic factors and thus serve as a baseline basin that could be used for comparative purposes. This contributes to a better quantification of aquifer dynamics in the Altiplano, allowing the evaluation of future protective measures to ensure survival of similar groundwater-dependent ecosystems in the area.

Evaluation of Surface Upward Longwave Radiation in the CMIP6 Models with Ground and Satellite Observations

Surface upward longwave radiation (SULR) is an indicator of thermal conditions over the Earth’s surface. In this study, we validated the simulated SULR from 51 Coupled Model Intercomparison Project (CMIP6) general circulation models (GCMs) through a comparison with ground measurements and satellite-retrieved SULR from the Clouds and the Earth’s Radiant Energy System, Energy Balanced and Filled (CERES EBAF). Moreover, we improved the SULR estimations by a fusion of multiple CMIP6 GCMs using multimodel ensemble (MME) methods. Large variations were found in the monthly mean SULR among the 51 CMIP6 GCMs; the bias and root mean squared error (RMSE) of the individual CMIP6 GCMs at 133 sites ranged from −3 to 24 W m−2 and 22 to 38 W m−2, respectively, which were higher than those found between the CERES EBAF and GCMs. The CMIP6 GCMs did not improve the overestimation of SULR compared to the CMIP5 GCMs. The Bayesian model averaging (BMA) method showed better performance in simulating SULR than the individual GCMs and simple model averaging (SMA) method, with a bias of 0 W m−2 and an RMSE of 19.29 W m−2 for the 133 sites. In terms of the global annual mean SULR, our best estimation for the CMIP6 GCMs using the BMA method was 392 W m−2 during 2000–2014. We found that the SULR varied between 386 and 393 W m−2 from 1850 to 2014, exhibiting an increasing tendency of 0.2 W m−2 per decade (p &lt; 0.05).

Predicting the Impact of Future Land Use and Climate Change on Potential Soil Erosion Risk in an Urban District of the Harare Metropolitan Province, Zimbabwe

Monitoring urban area expansion through multispectral remotely sensed data and other geomatics techniques is fundamental for sustainable urban planning. Forecasting of future land use land cover (LULC) change for the years 2034 and 2050 was performed using the Cellular Automata Markov model for the current fast-growing Epworth district of the Harare Metropolitan Province, Zimbabwe. The stochastic CA–Markov modelling procedure validation yielded kappa statistics above 80%, ascertaining good agreement. The spatial distribution of the LULC classes CBD/Industrial area, water and irrigated croplands as projected for 2034 and 2050 show slight notable changes. For projected scenarios in 2034 and 2050, low–medium-density residential areas are predicted to increase from 11.1 km2 to 12.3 km2 between 2018 and 2050. Similarly, high-density residential areas are predicted to increase from 18.6 km2 to 22.4 km2 between 2018 and 2050. Assessment of the effects of future climate change on potential soil erosion risk for Epworth district were undertaken by applying the representative concentration pathways (RCP4.5 and RCP8.5) climate scenarios, and model ensemble averages from multiple general circulation models (GCMs) were used to derive the rainfall erosivity factor for the RUSLE model. Average soil loss rates for both climate scenarios, RCP4.5 and RCP8.5, were predicted to be high in 2034 due to the large spatial area extent of croplands and disturbed green spaces exposed to soil erosion processes, therefore increasing potential soil erosion risk, with RCP4.5 having more impact than RCP8.5 due to a higher applied rainfall erosivity. For 2050, the predicted wide area average soil loss rates declined for both climate scenarios RCP4.5 and RCP8.5, following the predicted decline in rainfall erosivity and vulnerable areas that are erodible. Overall, high potential soil erosion risk was predicted along the flanks of the drainage network for both RCP4.5 and RCP8.5 climate scenarios in 2050.

Impacts of climate change on the streamflow of a large river basin in the Australian tropics using optimally selected climate model outputs

Climate change affects natural systems, leading to increased acceleration of global water cycle and substantial impacts on the productivity of tropical rivers and the several ecosystem functions they provide. However, the anticipated impacts of climate change in terms of frequency and intensity of extreme events (e.g., droughts and floods) on hydrological systems across regions could be substantially different. This study therefore aims to assess the impacts of climate change on the streamflow of a large river basin located in central Australia (Cooper Creek-Bulloo River Basin). Modified version of the hydrological model Hydrologiska Byråns Vattenbalansavdelning (HBV) was used in this study to generate daily streamflow. This model was first calibrated (2001–2010) and then validated for two independent periods (1993–1997 and 2011–2015). The model depicted a good performance in simulating observed streamflow. Climate projection data from multiple general circulation models (ACCESS1.0, CanESM2, CESM1-CAM5, CNRM-CM5, GFDL-ESM2M, HadGEM2-CC, MIROC5, NorESM1-M, ACCESS1-0, ACCESS1-3, CCSM4, CNRM-CM5, CSIRO-Mk3.6, GFDL-CM3, GFDL-ESM2M, HadGEM2, MIROC5, MPI-ESM-LR, and NorESM1-M) in various forms (raw, statistically downscaled, dynamically downscaled, and bias adjusted) were considered in this study. Results showed that three high resolution dynamically downscaled and bias adjusted models (ACCESS1-3, CNRM-CM5, and MPI-ESM-LR) from Terrestrial Ecosystem Research Network (TERN) dataset (v1.0.2) have better performance than other models considered, that is, in terms of capturing observed precipitation over the basin. Future climate projections of ensemble of these three models forced with RCP 4.5 and RCP 8.5 emission scenarios were then used to generate streamflow for 2050s (2040–2069) and 2080s (2070–2099). Results of the study indicated that mean annual precipitation was projected to decrease by up to −8% in 2050s and temperature was projected to increase by up to 4.66 °C in 2080s under the average and extreme emission scenarios, respectively. Mean annual, mean seasonal (December–February, March–May, June–August, September–November), and mean monthly streamflow were projected to decrease under different emission scenarios in 2050s and 2080s. These results indicate decreased water availability in the future as well as water cycle intensification. These changes in streamflow might have impacts on agriculture, natural ecosystem, and could lead to water restrictions. The outcome of this study can directly feed into frameworks for sustainable management of water resources and support adaptation strategies that rely on science and policy to improve water resources allocation in the region.

Spatio and temporal variations in population abundance and distribution of peach fruit fly, Bactrocera zonata (Saunders) during future climate change scenarios based on temperature driven phenology model

The peach fruit fly, Bactrocera zonata (Saunders) (Diptera: Tephritidae) is a poylphagous and serious insect pest of horticultural crops. The purpose of study was to understand the spatial and temporal variations in population abundance and distribution of B. zonata in response to climate change-based variations in temperature across the India. To examine the likely possibilities of changes in abundance and distribution of B. zonata, temperature driven process based phenology models were linked with climatic data of multiple General Circulation Model (eight models) and climate change scenarios (RCP 2.6, 4.5, 6.0 and 8.5) using the Insect Life Cycle Modeling (ILCYM) software. The risk indices (establishment, generation, and activity index) were mapped and quantified the changes in respect to locations, scenarios, models and times (2050 & 2070). The risk indices results revealed that, 1.73 (0.8–1.0 establishment risk), 14.15 (>16 high abundance) and 59.69% (>8.0 generation per year) area is projected to be highly suitable for B. zonata regarding establishment, abundance and generation indices, respectively in India under current climatic conditions. In spite of decreased permanent establishment (Establishment Risk Index > 0.6) in future climatic conditions, it is predicted that abundance and generation indices would increase in all the locations of the country. The variation in the results due to use of multiple GCM-scenario combinations suggested that choice of GCM and scenario combinations have impact on future prediction of the species. Overall, results indicate that B. zonata would be significant threat to horticultural crops in India. Therefore, present findings are of immensly useful to provide important information to design integrated pest management strategies and phytosanitary measurements for local, regional and national level to restrain the insect pest activity across different layers.

A flow-weighted ensemble strategy to assess the impacts of climate change on watershed hydrology

Evaluation of hydrological response to future climate change is essential for water quality risk assessment and adaptive management implementation within a watershed context. In this study, we present a modeling framework that integrates a hydrological model with projections of General Circulation Models (GCMs). Temperature and precipitation projections of six GCMs with two downscaling methods are used to force the Soil and Water Assessment Tool (SWAT) model in the Hamilton Harbour watershed in Ontario, Canada. A flow-based weighting strategy was developed to integrate the projections of multiple GCMs based on their ability to recreate empirical flow frequency distributions in multiple monitoring sites. Our study renders support to the ability of the weighted model ensemble to draw meaningful hydrological forecasts. Nonetheless, we also note that the ensemble strategy understates the frequency and magnitude of flow extremes, and therefore the domain that is collectively delineated by individual GCMs can still provide complementary planning information. Climate change is projected to trigger a distinct increase in air temperature and precipitation during the late winter-early spring period, which in turn will likely result in an earlier snowmelt and changes in the magnitude and timing of peak flow events. Analysis of the water cycle shows that the sensitivity of the individual hydrological components to climate change may vary along the urban-to-agriculture gradient. The projected declining soil–water content in agricultural catchments highlights the likelihood of more intensified drought conditions in the croplands. Evapotranspiration rates will likely increase across the entire watershed, whereas surface runoff could be reduced but less so in urbanized locations. Overall, our findings suggest that any future planning exercise to ameliorate the prevailing water quality conditions will only be insightful if we consider the interplay between climate change and urbanization processes in the area.

High-resolution hydrometeorological forecast in Southwest China based on a multi-layer nested WRF model

In this study, a high-resolution (5km:1km) regional hydrometeorological simulation (Weather Research and Forecasting, WRF) in Southwest China was evaluated by comparisons with the multiple General Circulation Model (multi-GCM) ensemble mean from Coupled Model Intercomparison Project phase 5 (CMIP5) and in-situ observation data, to prove its advantage to precisely delineate the regional complex topographical and climatic conditions. The temperature and precipitation were selected to evaluate the model performance skills. Simulations of the spatiotemporal rainfall and near-surface air temperature distribution across the entire research area and at four specific sites (Ganzi, Daofu, Jiulong Huili) were analyzed based on observational data from 2007–2010. Overall, both the WRF and multi-GCM demonstrated satisfactory capabilities in representing seasonal variation, but systematic biases remained. The regional average near-surface air temperature of WRF outputs had cold biases of −4.91, −1.96, −3.92 and −8.17°C in spring, summer, autumn and winter, respectively, and wet biases of 40.5 – 428.5 mm in cumulative precipitation over the four seasons. Overall, the multi-GCM means had consistent bias, but were closer to regional averages derived from in-situ data. At the four validation stations, the WRF outputs consistently performed better for temperature and precipitation according to the correlation coefficient, root-mean-square error, and index of agreement. The simulation capabilities identified herein can serve as a foundation for addressing WRF model biases and improving projection accuracy in the future.

Potential Climate Change Impacts On Imen Barley BYDV Resistant Variety Yields In Tunisia

Generally, Barley yields vary less under changing weather conditions than those of wheat and most other small grains. A field trials in National Institute of Agricultural Research of Tunisia (INRAT) experimental station in Kef region (Tunisia) during twelve cropping seasons (2008-2019) was conducted to study the relationships between agroclimatic indices and average yields of Imen barley BYDV resistant variety compared to the most important spring barley (Hordeum vulgare L.) varieties (Manel and Rihane). In this paper, results are used as a control to estimate potential impacts of climate change scenarios and pedological conditions on anticipated average yields and total production of these commodities in Kef region by the 2058 period. The agronomic crop model Cropsyst was used to assess the impacts of increased temperature on growth and development varieties shown below in Tunisia, and to examine possible adaptation strategies. The analysis was based on multi-year crop model simulations run with daily weather series 2020-2058 that allowed two average yield levels: grain and biomass. The impacts of 0.2 °C temperature rise obtained by weather generator ClimGen included in Cropsyst on potential yields are positive and induced an increase of 14.4 %/ha for Imen in comparison with Manel 13.7 % and Rihane 10.1 %. Based on a range of available heat units projected by multiple General Circulation Model experiments, a climate change scenario representing +1 °C temperature rise was also applied for the same period and using the same generator. With 1°C temperature rise, average yieldsachievable in field trials could decrease in long term by about 0,16 q/ha (Rihane) and 0.46 t/ha (Manel) (4.7 % and 14 %). Cropsyst yield data, however, indicate an increase of 0.5 % with +1°C temperature rise suggesting the resilience of Imen barley grain production, compared to the two varieties Manel and Rihane.

Optimization Assessment of Projection Methods of Climate Change for Discrepancies between North and South China

Downscaling methods have been widely used due to the coarse and biased outputs of general circulation models (GCMs), which cannot be applied directly in regional climate change projection. Hence, appropriate selection of GCMs and downscaling methods is important for assessing the impacts of climate change. To explicitly explore the influences of multi-GCMs and different downscaling methods on climate change projection in various climate zones, the Heihe River Basin (HRB) and the Zhanghe River Basin (ZRB) were selected in this study to represent the north arid region and the south humid region in China, respectively. We first evaluated the performance of multi-GCMs derived from Coupled Model Inter-comparison Project Phase 5 (CMIP5) in the two regions based on in-situ measurements and the 40 year European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40) data. Subsequently, to construct appropriate climate change projection techniques, comparative analysis using two statistical downscaling methods was performed with consideration of the significant north–south meteorological discrepancies. Consequently, specific projections of future climate change for 2021–2050 under three representative concentration pathway (RCP) scenarios (RCP2.6, RCP4.5, and RCP8.5) were completed for the HRB and ZRB, including daily precipitation, maximum air temperature, and minimum air temperature. The results demonstrated that the score-based method with multiple criteria for performance evaluation of multiple GCMs more accurately captured the spatio-temporal characteristics of the regional climate. The two statistical downscaling methods showed respective advantages in arid and humid regions. The statistical downscaling model (SDSM) showed more accurate prediction capacities for air temperature in the arid-climate HRB, whereas model output statistics (MOS) better captured the probability distribution of precipitation in the ZRB, which is characterized by a humid climate. According to the results obtained in this study, the selection of appropriate GCMs and downscaling methods for specific climate zones with different meteorological features significantly impact regional climate change projection. The statistical downscaling models developed and recommended for the north and south of China in this study provide scientific reference for sustainable water resource management subject to climate change.