High-resolution Climate Model Research Articles

Influence of Model Resolution on Wind Energy Simulations over Tibetan Plateau Using CMIP6 HighResMIP

The assessment of wind energy resources is critical for the transition from fossil fuel to renewable energy sources. Using the outputs from high-resolution global climate models (GCMs), such as the High Resolution Model Intercomparison Project (HighResMIP) of the Coupled Model Intercomparison Project Phase 6 (CMIP6), has become one of the most important tools in wind energy research. This study evaluated the reliability of the 22 GCMs available in the HighResMIP-PRIMAVERA project by simulating the wind energy climatology and variability over the Tibetan Plateau (TP) with reference to observations and investigated the differences in performance of the GCMs between high-resolution (HR) and low-resolution (LR) simulations. The results show that most models performed relatively well in simulating the probability distribution of the observed wind speed over the TP, but nearly half of the models generally underestimated the wind speed, whereas the others tended to overestimated the wind speed. Compared with the wind speed, the GCMs showed larger biases in reproducing the wind power density (WPD) and other wind energy resources, whereas the biases in multi-model ensembles were relatively smaller than those in most individual models. With respect to interannual variability, both the HR and LR models failed to capture interannual variations in WPD over the TP. Furthermore, more than half of the HR GCMs had a reduced bias relative to the corresponding LR GCMs, indicating the good performance of most HR models in simulating wind energy resources over the TP in terms of spatial pattern and temporal variability. However, the overall performance of HR GCMs varied among models, which suggests that solely improving the horizontal resolution is not sufficient to completely solve the uncertainties and deficiencies in the simulation of wind energy over complex terrain.

Improvements in the land surface configuration to better simulate seasonal snow cover in the European Alps with the CNRM-AROME (cycle 46) convection-permitting regional climate model

Abstract. Snow cover modeling remains a major challenge in climate and numerical weather prediction (NWP) models even in recent versions of high-resolution coupled surface–atmosphere (i.e., at kilometer scale) regional models. Evaluation of recent climate simulations, carried out as part of the WCRP-CORDEX Flagship Pilot Study on Convection (FPSCONV) with the CNRM-AROME convection-permitting regional climate model at 2.5 km horizontal resolution, has highlighted significant snow cover biases, severely limiting its potential in mountain regions. These biases, which are also found in AROME numerical weather prediction (NWP) model results, have multiple causes, involving atmospheric processes and their influence on input data to the land surface models in addition to deficiencies of the land surface model itself. Here we present improved configurations of the SURFEX-ISBA land surface model used in CNRM-AROME. We thoroughly evaluated these configurations on their ability to represent seasonal snow cover across the European Alps. Our evaluation was based on coupled simulations spanning the winters of 2018–2019 and 2019–2020, which were compared against remote sensing data and in situ observations. More specifically, the study tests the influence of various changes in the land surface configuration, such as the use of multi-layer soil and snow schemes, the division of the energy balance calculation by surface type within a grid cell (multiple patches), and new physiographic databases and parameter adjustments. Our findings indicate that using only more detailed individual components in the surface model did not improve the representation of snow cover due to limitations in the approach used to account for partial snow cover within a grid cell. These limitations are addressed in further configurations that highlight the importance, even at kilometer resolution, of taking into account the main subgrid surface heterogeneities and improving representations of interactions between fractional snow cover and vegetation. Ultimately, we introduce a land surface configuration, which substantially improves the representation of seasonal snow cover in the European Alps in coupled CNRM-AROME simulations. This holds promising potential for the use of such model configurations in climate simulations and numerical weather prediction both for AROME and other high-resolution climate models.

Identification of thresholds and key drivers on Water Use Efficiency in different maize ecoregions in Yellow River Basin of China

Identifying the constrains on water use efficiency (WUE) of crops along a wet-to-dry gradient is important due to irrigation water scarcity, as well as the increasing drought risk under climate change in China. This study coupled five high-resolution climate models from Coupled Model Intercomparison Project Phase 6 (CMIP6) with the Decision Support System for Agrotechnology Transfer (DSSAT)-CERES-Maize model to quantify drought risk and the drivers affecting WUE in five major maize ecoregions of the Yellow River Basin (YRB) under three future scenarios (SSP126, SSP370, and SSP585) for both the historical baseline (1985-2014) and three future periods: 2021-2040 (2030s), 2041-2070 (2050s), and 2071-2100 (2080s). And a bias correction method was implemented for the crop model to analyze optimal WUE thresholds for maize across varying dry-wet gradients. The results indicated that future drought risk will likely persist in the YRB under all scenarios, but with regional differences in drought severity and frequency. The southwestern region (V) experienced the highest frequency of drought (62.50%-SSP126), while the northwestern region (III) exhibited the lowest frequency (33.00%-SSP585) in 2030s, and 83.30% of areas in the southwestern (V) showed significant wetting in the 2080s under SSP126. The bias-corrected CERES-Maize model effectively simulated crop yield and evapotranspiration (ET), resulting in an average reduction of 4.00% and 9.73% in normalized root mean square error (nRMSE) respectively. Distinct WUE thresholds ranging from 1.96 to 8.41 kg ha−1 mm-1 were observed across various scenarios-periods in the maize regions, mostly under slight and moderate dry/wet conditions. Notably, all SSP585 scenarios demonstrated a decrease in WUE thresholds compared to the baseline. Across all scenarios and periods, WUE was mainly driven by yield in the eastern regions (I and II) but by ET in the western regions (III, IV, and V). These findings suggest that regions experiencing varying degrees of drought severity should undergo differentiated management and optimization of agricultural practices to improve WUE under future climate scenarios.

Resolving the ubiquitous small-scale semi-permanent features of the general ocean circulation: a multiplatform observational approach

Abstract Based on twenty years of Argo and ship/animal-borne/glider hydrographic profile data, we derive a new high resolution hydrographic Atlas and associated circulation field for the oceans above 2000 dbar. Satellite altimetric observations are used to explicitly regress out eddy noise in the fit, greatly reducing one of the major sources of noise. Geostrophic shears are found from the fitted geopotential anomaly fields. Ekman velocities are estimated using satellite wind stresses. Both Argo trajectory observations at 1000 dbar and surface drifter observations are used to reference geostrophic shears derived from the Atlas hydrography. Surface drifter velocities are analyzed with an additional wind-friction term to remove the wind-related flow. Agreement between the surface geostrophic (referenced to Argo trajectories) and drifter-based surface velocity is high at both large and mesoscales, lending confidence to the derived geostrophic circulation fields. The Atlas reveals standing mesoscale eddies and meanders in western boundary systems, and the braided jet structure of the Antarctic Circumpolar Current. In the interior, the upper ocean flow consists of a highly baroclinic large-scale Sverdrup flow and smaller scale (~200 km width) semi-zonal jets, which are more barotropic (low vertical shear) and have an average zonal width of around 2000 km. These semi-zonal jets are globally ubiquitous - found in all basins pole-to-pole. The many permanent mesoscale features of the mean general circulation contrasts with that predicted by theories of the large-scale flow in simplified flat-bottomed domains. The new Atlas presents a new opportunity to benchmark modern high resolution ocean and climate models.

Projected changes of thermal, rainfall and drought indices in Morocco from high resolution climate models

Abstract Over the past decade, global changes in temperature, average and extreme precipitation have been observed many times. The increase in temperature or even the change in precipitation systems is directly affect extreme weather events, for example, heat waves, heavy rainfall, storms, and droughts are becoming more frequent and more powerful worldwide due to climate change caused by several factors including human activity. This is why there is a necessity to monitor future changes in climate extremes. The objective of this paper is to evaluate the future changes in thermal and rainfall extremes projected by the four climate models over time horizon 2041-2060 relative to the 1981-2010 reference period, based on the 5 climate indices, including Tmm, Tx90p, WSDI, PRCPTOT and SPI. The projections are given under the RCP 4.5 medium scenario. The analysis in thermal aspect shows that Morocco could experience by 2041-2060, a significant warming, has manifested by an increase in average temperature. Future changes in rainfall indices show that the spatial distribution of rainfall in Morocco could change significantly by 2041-2060 compared to the reference period 1971-2010. The four climate models agree on a decrease in annual precipitation that could affect most of the Kingdom. In the same way to dryness, the future changes of the SPI index that characterizes the drought, indicates a move to a dry climate.

Evaluation of the Aggregation Efficiency Modeling at Colder Atmospheric Temperatures in Comparison to Satellite Observations

Abstract Aggregation efficiency in the upper troposphere is highly uncertain because of the lack of laboratory experiments and aircraft measurements, especially at atmospheric temperatures below −30°C. Aggregation is physically broken down into collision and sticking. In this study, theory-based parameterizations for the collision efficiency and sticking efficiency are newly implemented into a double-moment bulk cloud microphysics scheme. Satellite observations of the global ice cloud distribution are used to evaluate the aggregation efficiency modeling. Sensitivity experiments of 9-day global simulations using a high-resolution climate model show that the use of collision efficiency parameterization causes a slight increase in the cloud ice amount above the freezing level over the tropics to midlatitudes and that the use of our new sticking efficiency parameterization causes a significant increase in the cloud ice amount and a slight decrease in the snow amount particularly in the upper troposphere over the tropics. The increase/decrease in the cloud ice/snow amount in the upper troposphere over the tropics is consistent with the vertical profile of radar echoes. Moreover, the ice fraction of the cloud optical thickness is still underestimated worldwide. Finally, the cloud radiative forcing increases over the tropics to reduce the bias in the radiation budget. These results indicate that our new aggregation efficiency modeling reasonably functions even at atmospheric temperatures below −30°C; however, further improvements in the ice cloud modeling are needed. Significance Statement Long-standing biases in the radiative budget in climate models indicate the existence of a missing mechanism to realistically represent the ice cloud growth in the upper troposphere. This study focuses on aggregation efficiency, which has been assumed to be a tuning parameter to optimize the global radiative budget. Therefore, this study employs a theory-based parameterization to calculate the aggregation efficiency. According to the parameterization, aggregation efficiency in high clouds varies by the growth stage of the individual ice particles. As a result, small ice crystals are likely to grow more slowly, and the lifetime of cirrus clouds is prolonged to enhance cloud radiative forcing, particularly over the tropics. These results are promising for reducing the biases observed in climate models.

Robust future projections of global spatial distribution of major tropical cyclones and sea level pressure gradients

Despite the profound societal impacts of intense tropical cyclones (TCs), prediction of future changes in their regional occurrence remains challenging owing to climate model limitations and to the infrequent occurrence of such TCs. Here we reveal projected changes in the frequency of major TC occurrence (i.e., maximum sustained wind speed: ≥ 50 m s−1) on the regional scale. Two independent high-resolution climate models projected similar changes in major TC occurrence. Their spatial patterns highlight an increase in the Central Pacific and a reduction in occurrence in the Southern Hemisphere—likely attributable to anthropogenic climate change. Furthermore, this study suggests that major TCs can modify large-scale sea-level pressure fields, potentially leading to the abrupt onset of strong wind speeds even when the storm centers are thousands of kilometers away. This study highlights the amplified risk of storm-related hazards, specifically in the Central Pacific, even when major TCs are far from the populated regions.

Role of the Labrador Current in the Atlantic Meridional Overturning Circulation response to greenhouse warming

Anthropogenic warming is projected to enhance Arctic freshwater exportation into the Labrador Sea. This extra freshwater may weaken deep convection and contribute to the Atlantic Meridional Overturning Circulation (AMOC) decline. Here, by analyzing an unprecedented high-resolution climate model simulation for the 21st century, we show that the Labrador Current strongly restricts the lateral spread of freshwater from the Arctic Ocean into the open ocean such that the freshwater input has a limited role in weakening the overturning circulation. In contrast, in the absence of a strong Labrador Current in a climate model with lower resolution, the extra freshwater is allowed to spread into the interior region and eventually shut down deep convection in the Labrador Sea. Given that the Labrador Sea overturning makes a significant contribution to the AMOC in many climate models, our results suggest that the AMOC decline during the 21st century could be overestimated in these models due to the poorly resolved Labrador Current.

Impact of climate change on major floods flowing into the Georges River estuary, Australia

Coastal flooding induced by storm surges and heavy rainfall is one of the most frequent climate-related natural hazards along the southeast Australian coast, home to more than 55% of the Australian population. Flooding in this densely populated region is a threat to public safety, coastal infrastructure, ecological systems and the economy. Although climate change is expected to cause an increase in major floods, few studies have quantified the potential changes in flood severity. This study quantifies the changes in flood peak discharge flowing to the Georges River estuary in Australia due to climate-change. An event-based hydrological model, Watershed Bounded Network Model (WBNM), was used to predict flood discharge. This hydrological model was forced by rainfall data obtained from the New South Wales and Australian Capital Territory Regional Climate Modelling Project version 1.5 (NARCliM1.5) for both historical and the Representative Concentration Pathway 8.5 (RCP8.5) conditions. Model calibration for the floods of March 1978 and March 2022 achieved a general agreement between the predicted and observed hydrographs, with an overall average 14% error in the peak values, further demonstrating that the modelling approach is generally reliable in projections of flood severity. Using high resolution climate model projections, the present study observed an increase of 22% in the model ensemble average from historical conditions to the RCP8.5 scenario for the 20-year average recurrence interval (ARI) 24 h extreme rainfall. This heightened extreme rainfall consequently resulted in the changes in flood discharge with an average rise of 55%. This study provides specific assessment of climate-generated risks for densely-populated regions, especially those on Australian east coast. Global studies have suggested that extreme precipitation events will increase under climate change. This study supports and enhances these assertions by using high resolution downscaling to quantify the specific changes within a large catchment.

Atlantic Meridional Overturning Circulation Decline: Tipping Small Scales under Global Warming.

The Atlantic circulation is a key component of the global ocean conveyor that transports heat and nutrients worldwide. Its likely weakening due to global warming has implications for climate and ecology. However, the expected changes remain largely uncertain as low-resolution climate models currently in use do not resolve small scales. Although the large-scale circulation tends to weaken uniformly in both the low-resolution and our high-resolution climate model version, we find that the small-scale circulation in the North Atlantic changes abruptly under global warming and exhibits pronounced spatial heterogeneity. Furthermore, the future Atlantic Ocean circulation in the high-resolution model version expands in conjunction with a sea ice retreat and strengthening toward the Arctic. Finally, the cutting-edge climate model indicates sensitive shifts in the eddies and circulation on regional scales for future warming and thus provides a benchmark for next-generation climate models that can get rid of parametrizations of unresolved scales.

Poleward migration of tropical cyclones over the western North Pacific in the CMIP6-HighResMIP models constrained by observations

Tropical cyclones (TCs) have experienced poleward migration in recent years, but whether this exists in future projections with high-resolution climate models remains unclear. This study investigates the poleward migration of TCs over the western North Pacific (WNP) using CMIP6-HighResMIP models. We first assess the model performance in TC genesis frequency and latitude, which differ greatly from the observations, especially in winter and spring due to misinterpretation of extratropical storms. In this study, we put forward a revised constrained detection method based on the sea surface temperature (SST) and the atmospheric conditions to resolve this bias. Results indicate that the revised detection method has good performance in capturing the annual cycle of TC genesis frequency and latitude. Future projections constrained by this method show that the latitude of TC genesis and lifetime maximum intensity (LMI) both undergo a poleward shift, with the former being more significant. Spatial changes in the dynamic potential genesis index and large-scale environment could explain this shift. The regional changes of Hadley circulation and the role of global warming and internal variability are also discussed.

An integrated approach for the decision of wave energy converter deployment based on forty-five-years high-resolution wave climate modeling

Climate change is driving the urgent need for sustainable and renewable energy sources to mitigate its impacts and reduce greenhouse gas emissions. Ocean wave energy, as one of the promising alternatives to fossil fuels, plays a critical role in providing renewable energy in coastal areas. To fully exploit regional wave energy potential, various performance metrics of wave energy converters (WECs) need to be considered to optimize location decisions and WEC categories. This study introduces an integrated approach by incorporating a novel factor, the maximum consecutive cut-off duration of WECs due to extreme weather conditions. It aims to enhance the efficiency of WEC selection and deployment location optimization in the southern and eastern Asian seas. Through 45 years of high-resolution (up to 250 m) ocean wave simulations, conducted using WaveWatch III ®(WW3) on unstructured mesh, Wave Dragon was identified as providing the highest index. This study suggests that placing WECs along the coastlines of the South China Sea, the eastern and northern Bay of Bengal, the western sector of the Malay Archipelago, and parts of the Arafura Sea may result in the most efficient conversion of wave energy in the southern and eastern Asian seas. While the proposed approach alone may not dictate deployment decisions, this study provides an applicable index to potentially rank WEC deployment options in the eastern and southern Asian seas, by considering both exploitable wave energy production, and long-term stability.

Hierarchical Autoencoder-Based Lossy Compression for Large-Scale High-Resolution Scientific Data

Lossy compression has become essential an important technique to reduce data size in many domains. This type of compression is especially valuable for large-scale scientific data, whose size ranges up to several petabytes. Although Autoencoder-based models have been successfully leveraged to compress images and videos, such neural networks have not widely gained attention in the scientific data domain. Our work presents a neural network that not only significantly compresses large-scale scientific data, but also maintains high reconstruction quality. The proposed model is tested with scientific benchmark data available publicly and applied to a large-scale high-resolution climate modeling data set. Our model achieves a compression ratio of 140 on several benchmark data sets without compromising the reconstruction quality. 2D simulation data from the High-Resolution Community Earth System Model (CESM) Version 1.3 over 500 years are also being compressed with a compression ratio of 200 while the reconstruction error is negligible for scientific analysis.

We still need a climate ‘CERN’

Tim Palmer says that we must pool our resources to produce high-resolution climate models that societies can use, before it is too late.

Projected changes in heat, extreme precipitation, and their spatially compound events over China’s coastal lands and seas through a high-resolution climate models ensemble

China’s coastal lands and seas are highly susceptible to the changing environment due to their dense population and frequent economic activities. These areas experience more significant impacts from climate change-induced extreme events than elsewhere. The most noticeable effects of climate change are extreme high temperatures and extreme precipitation. We employ an ensemble of RCMs (Regional Climate Models) to investigate and project changes in temperature, precipitation, and Compound Heat-Precipitation Extreme events (CHPEs) over selected China’s coastal lands and seas for both historical (1985–2004) and future periods (2080–2099). The multi-model ensemble projects that daily temperature extremes will increase by 2.9 °C to 5.4 °C across China’s coastal lands and seas, with land areas showing a higher temperature increase than marine areas. Extreme precipitation shows a high geographical heterogeneity with a 2.8–3.9 mm d−1 reduction over the 15–25°N marine areas while a 2.2–5.4 mm d−1 increment over the 25°N-35°N land areas. We use the Clausius–Clapeyron relationship to reveal that the peak of daily extreme precipitation will increase by 2–7 mm d−1 and the temperature at which extreme precipitation peaks will increase by 2 °C to 6 °C by warming. The land area of 25–30°N has the highest peak precipitation increase of 9.87 mm d−1 and a peak temperature increase of 6 °C. As precipitation extremes intensify with daily temperature extremes increase, CHPEs are projected to occur more frequently over both land and marine areas. Compared with the historical period, the frequency of CHPEs will increase by 40.9%-161.2% over marine areas, and by 36.2%-163.6% over land areas in the future. The 15–20°N area has the highest frequency increase of CHPE events, and the 25–30°N area has the largest difference in frequency increase under two different scenarios. It indicated that the 25–30°N area will be more easily affected by climate change.

Identifying atmospheric rivers and their poleward latent heat transport with generalizable neural networks: ARCNNv1

Abstract. Atmospheric rivers (ARs) are extreme weather events that can alleviate drought or cause billions of US dollars in flood damage. By transporting significant amounts of latent energy towards the poles, they are crucial to maintaining the climate system's energy balance. Since there is no first-principle definition of an AR grounded in geophysical fluid mechanics, AR identification is currently performed by a multitude of expert-defined, threshold-based algorithms. The variety of AR detection algorithms has introduced uncertainty into the study of ARs, and the thresholds of the algorithms may not generalize to new climate datasets and resolutions. We train convolutional neural networks (CNNs) to detect ARs while representing this uncertainty; we name these models ARCNNs. To detect ARs without requiring new labeled data and labor-intensive AR detection campaigns, we present a semi-supervised learning framework based on image style transfer. This framework generalizes ARCNNs across climate datasets and input fields. Using idealized and realistic numerical models, together with observations, we assess the performance of the ARCNNs. We test the ARCNNs in an idealized simulation of a shallow-water fluid in which nearly all the tracer transport can be attributed to AR-like filamentary structures. In reanalysis and a high-resolution climate model, we use ARCNNs to calculate the contribution of ARs to meridional latent heat transport, and we demonstrate that this quantity varies considerably due to AR detection uncertainty.

The effect of explicit convection on simulated malaria transmission across Africa.

Malaria transmission across sub-Saharan Africa is sensitive to rainfall and temperature. Whilst different malaria modelling techniques and climate simulations have been used to predict malaria transmission risk, most of these studies use coarse-resolution climate models. In these models convection, atmospheric vertical motion driven by instability gradients and responsible for heavy rainfall, is parameterised. Over the past decade enhanced computational capabilities have enabled the simulation of high-resolution continental-scale climates with an explicit representation of convection. In this study we use two malaria models, the Liverpool Malaria Model (LMM) and Vector-Borne Disease Community Model of the International Centre for Theoretical Physics (VECTRI), to investigate the effect of explicitly representing convection on simulated malaria transmission. The concluded impact of explicitly representing convection on simulated malaria transmission depends on the chosen malaria model and local climatic conditions. For instance, in the East African highlands, cooler temperatures when explicitly representing convection decreases LMM-predicted malaria transmission risk by approximately 55%, but has a negligible effect in VECTRI simulations. Even though explicitly representing convection improves rainfall characteristics, concluding that explicit convection improves simulated malaria transmission depends on the chosen metric and malaria model. For example, whilst we conclude improvements of 45% and 23% in root mean squared differences of the annual-mean reproduction number and entomological inoculation rate for VECTRI and the LMM respectively, bias-correcting mean climate conditions minimises these improvements. The projected impact of anthropogenic climate change on malaria incidence is also sensitive to the chosen malaria model and representation of convection. The LMM is relatively insensitive to future changes in precipitation intensity, whilst VECTRI predicts increased risk across the Sahel due to enhanced rainfall. We postulate that VECTRI's enhanced sensitivity to precipitation changes compared to the LMM is due to the inclusion of surface hydrology. Future research should continue assessing the effect of high-resolution climate modelling in impact-based forecasting.

Projected Changes in Mean and Extreme Precipitation over Northern Mexico

Abstract Northern Mexico is home to more than 32 million people and is of significant agricultural and economic importance for the country. The region includes three distinct hydroclimatic regions, all of which regularly experience severe dryness and flooding and are highly susceptible to future changes in precipitation. To date, little work has been done to characterize future trends in either mean or extreme precipitation over northern Mexico. To fill this gap, we investigate projected precipitation trends over the region in the NA-CORDEX ensemble of dynamically downscaled simulations. We first verify that these simulations accurately reproduce observed precipitation over northern Mexico, as derived from the Multi-Source Weighted-Ensemble Precipitation (MSWEP) product, demonstrating that the NA-CORDEX ensemble is appropriate for studying precipitation trends over the region. By the end of the century, simulations forced with a high-emissions scenario project that both mean and extreme precipitation will decrease to the west and increase to the east of the Sierra Madre highlands, decreasing the zonal gradient in precipitation. We also find that the North American monsoon, which is responsible for a substantial fraction of the precipitation over the region, is likely to start later and last approximately three weeks longer. The frequency of extreme precipitation events is expected to double throughout the region, exacerbating the flood risk for vulnerable communities in northern Mexico. Collectively, these results suggest that the extreme precipitation-related dangers that the region faces, such as flooding, will increase significantly by the end of the century, with implications for the agricultural sector, economy, and infrastructure. Significance Statement Northern Mexico regularly experiences severe flooding and its important agricultural sector can be heavily impacted by variations in precipitation. Using high-resolution climate model simulations that have been tested against observations, we find that these hydroclimate extremes are likely to be exacerbated in a warming climate; the dry (wet) season is projected to receive significantly less (more) precipitation (approximately ±10% by the end of the century). Simulations suggest that some of the changes in precipitation over the region can be related to the North American monsoon, with the monsoon starting later in the year and lasting several weeks longer. Our results also suggest that the frequency of extreme precipitation will increase, although this increase is smaller than that projected for other regions, with the strongest storms becoming 20% more frequent per degree of warming. These results suggest that this region may experience significant changes to its hydroclimate through the end of the century that will require significant resilience planning.

Deep learning modeling framework for multi-resolution streamflow generation

Abstract Generating continuous streamflow information through integrated climate-hydrology modeling at fine spatial scales of the order of a few kilometers is often challenged by computational costs associated with running high-resolution (HR) climate models. To address this challenge, the present study explores deep learning approaches to generate HR streamflow information from that at low resolution (LR), based on runoff generated by climate models. Two sets of daily streamflow simulations spanning 10 years (2011–2020), at LR (50 km) and HR (5 km), for the Ottawa River basin, Canada, are employed. The proposed deep learning model is trained using upscaled features derived from LR streamflow simulation for the 2011–2018 period as input and the corresponding HR streamflow simulation as the target; data for 2019 are used for validation. The model estimates for the year 2020, when compared with unseen HR data for the same year, suggest good performance, with differences in monthly mean values for different accumulation area categories in the −0.7–5% range and correlation coefficients for streamflow values for the same accumulation area categories in the 0.92–0.96 range. The developed framework can be ported to other watersheds for generating similar information, which is required in climate change adaptation studies.

Assessing rainfall erosivity changes over China through a Bayesian averaged ensemble of high-resolution climate models

Spatiotemporal variation in rainfall erosivity resulting from changes in rainfall characteristics due to climate change has implications for soil erosion in developing countries. To promote soil and water conservation planning, it is essential to understand past and future changes in rainfall erosivity and their implications on a national scale. In this study, we present an approach that uses a Bayesian model averaging (BMA) method to merge multiple regional climate models (RCMs), thereby improving the reliability of climate-induced rainfall erosivity projections. Our multi-climate model and multi-emission scenario approach utilize five RCMs and two Representative Concentration Pathways (RCP4.5 and RCP8.5) scenarios for the baseline period (1986–2005) and future periods (2071–2090) to characterize the spatiotemporal projection of rainfall erosivity and assess variations in China. Our results indicate that the two models outperform other models in reproducing the spatial distribution and annual cycle of rainfall erosivity in China. Moreover, we found an increasing trend in the annual rainfall erosivity from the baseline climate up to the RCMs for all models, with an average change in erosivity of approximately 10.9% and 14.6% under RCP4.5 and RCP8.5, respectively. Our BMA results showed an increase in the absolute value of rainfall erosivity by 463.3 and 677.0 MJ·mm·hm−2·h−1, respectively, in the South China red soil region and the Southwest China karst region under the RCP8.5 scenario. This increase indicates that climate warming will significantly enhance the potential erosion capacity of rainfall in these regions. Additionally, our study revealed that the Southwest China karst region and the Northwest China Loess Plateau region are more sensitive to radiation forcing. To mitigate the risk of soil erosion caused by climate change, it is necessary to consider changes in rainfall erosivity, local soil conditions, vegetation coverage, and other factors in different regions and take appropriate soil and water conservation measures.