Downscaling Research Articles

Climate-resilient urban drainage planning: An approach using a GIS-based SCS-CN model

ABSTRACT Inexorable urbanization continues apace across the world and urban flooding in megacities is now frequently evidenced due to extreme rainfall events due to climate change (CC). Climate-resilient urban drainage planning is critical towards making sustainable cities or any new urbanization. This paper presents an approach through an insightful assessment of climate resilient urban drainage applying GIS-based Soil Conservation Service-Curve Number (SCS-CN) model of a new urban growth of megacity Dhaka, Bangladesh. A precise DEM (Digital Elevation Model) of the study area has been used for catchment delineation using ArcSWAT. Localized climate anomalies of rainfall of around 11% have been identified during monsoon from statistical downscaling and included in the cumulative rainfall event of 5 days. The effect of historical and CC-induced rainfall have been used to identify and map the peak discharges of sub-catchments considering the return period of 5-day cumulative rainfall for 10, 25, and 100 years of the urban catchment for both existing and future land-use scenarios accounting for the change in CN. The varying results of the peak discharges of the sub-catchments for resilient drainage planning is not only dependent on the increase in rainfall but also the combined response of the land-use and soil profile.

Intermediate complexity atmospheric modeling in complex terrain: is it right?

Dynamic downscaling of atmospheric forcing data to the hectometer resolution has shown increases in accuracy for landsurface models, but at great computational cost. Here we present a validation of a novel intermediate complexity atmospheric model, HICAR, developed for hectometer scale applications. HICAR can run more than 500x faster than conventional atmospheric models, while containing many of the same physics parameterizations. Station measurements of air temperature, wind speed, and radiation, in combination with data from a scanning Doppler wind LiDAR, are compared to 50 m resolution HICAR output during late spring. We examine the model’s performance over bare ground and melting snow. The model shows a smaller root mean squared error in 2 m air temperature than the driving model, and approximates the 3D flow features present around ridges and along slopes. Timing and magnitude of changes in shortwave and longwave radiation also show agreement with measurements. Nocturnal cooling during clear nights is overestimated at the snow covered site. Additionally, the thermal wind parameterization employed by the model typically produces excessively strong surface winds, driven in part by this excessive nocturnal cooling over snow. These findings highlight the utility of HICAR as a tool for dynamically downscaling forcing datasets, and expose the need for improvements to the snow model used in HICAR.

A seasonal snowpack model forced with dynamically downscaled forcing data resolves hydrologically relevant accumulation patterns

The Mountain snowpack stores months of winter precipitation at high elevations, supplying snowmelt to lowland areas in drier seasons for agriculture and human consumption worldwide. Accurate seasonal predictions of the snowpack are thus of great importance, but such forecasts suffer from major challenges such as resolving interactions between forcing variables at high spatial resolutions. To test novel approaches to resolve these processes, seasonal snowpack simulations are run at different grid resolutions (50 m, 100 m, 250 m) and with variable forcing data for the water year 2016/2017. COSMO-1E data is either dynamically downscaled with the High-resolution Intermediate Complexity Atmospheric Research (HICAR) model or statistically downscaled to provide forcing data for snowpack simulations with the Flexible Snowpack Model (FSM2oshd). Simulations covering complex terrain in the Swiss Alps are carried out with the operational settings of the FSM2oshd model or with a model extension including wind- and gravitational-induced snow transport (FSM2trans). The simulated snow height is evaluated against observed snow height collected during LiDAR flights in spring 2017. Observed spatial snow accumulation patterns and snow height distribution are best matched with simulations using dynamically downscaled data and the FSM2trans model extension, indicating the importance of both accurate meteorological forcing data and snow transport schemes. This study demonstrates for the first time the effects of applying dynamical downscaling schemes to snowpack simulations at the seasonal and catchment scale.

Deep‐learning‐based downscaling of precipitation in the middle reaches of the Yellow River using residual‐based CNNs

AbstractThe middle reaches of the Yellow River (MRYR), located in northern China, are the transition zone between semi‐arid and semi‐humid climates. As one of the climate‐sensitive regions in China, MRYR has a fragile ecological environment and serious soil loss, which leads to geological disasters such as landslides, collapses, and mudslides caused by extreme precipitation. However, scarceness of high‐resolution precipitation data over MRYR limits assessment of the environmental impacts caused by climate change, especially for extreme precipitation. In this article, we design a Residual‐in‐Residual Dense Block based Network (RRDBNet) model for the statistical downscaling of precipitation in MRYR, and compare the proposed RRDBNet with a generalized linear regression model (GLM) and two popular deep‐learning‐based models. The multi‐level residuals and dense connectivity strategies introduced in RRDBNet help it to learn more abstract features and complex nonlinear relationships among climate variables to improve downscaling performance. The results show that the proposed RRDBNet has good performance in precipitation simulations, which can reproduce the spatial–temporal characteristics of high‐resolution precipitation well. RRDBNet reduces the root‐mean‐squared error (RMSE) by 19% and improves the Pearson correlation coefficient (CC) by 6% relative to GLM for climatology mean precipitation. Especially, RRDBNet has substantial improvements in extreme precipitation compared with other models. It reduces RMSE by 58% (79%) and improves CC by 38% (145%) relative to GLM for R95P (R99P), where R95P and R99P represent extreme precipitation and very extreme precipitation, respectively. For the probability density function of daily precipitation, it is further demonstrated that RRDBNet performs better as regards extreme precipitation frequency. Our results suggest that statistical downscaling based on RRDBNet may be an effective tool for historical and future climate simulations from global climate models.

Future Changes in Regional Tropical Cyclone Wind, Precipitation, and Flooding Using Event‐Based Downscaling

AbstractUnderstanding changes in the hazard component of climate risk is important to inform societal resilience planning in a changing climate. Here, we examine local changes in wind speed, rainfall, and flooding related to tropical cyclones (TCs) and compare them across statistical and dynamical modeling approaches. Our focus region is the Delaware River Basin, located in the northeastern United States. We pair event‐based downscaling with large ensemble climate model information to capture the details of extreme TC wind, rain, and flooding, and their likelihood, in a changing climate. We identify local TCs in the Community Earth System Model 2 Large Ensemble (CESM2‐LENS). We find fewer TCs in the future, but these future storms have higher wind speeds and are wetter. We also find that TCs produce heavier 3‐day precipitation distributions than all other summertime weather events, with TCs constituting a larger percentage of the upper tail of the full precipitation distribution. With this information, we identify a small collection of 200‐year return events and compare the resulting TC rain and wind across dynamical and statistical downscaling methods. We find that dynamical downscaling produces peak rain rates far higher than CESM or the statistical downscaling method. It can also produce quite different future changes in precipitation totals for the small set of events considered here. This leads to vastly different flood responses. Overall, our results highlight the need to interpret future changes of event‐based simulations in the context of downscaling method limitations.

A Method for Elevated Ducts Refinement Based on Convolutional Neural Network

AbstractElevated duct (EleD) is an abnormal atmospheric refraction structure with a suspended trapped layer. The precise and highly resolved elevated duct‐height‐based data (EleDH) is crucial for radio communication systems, especially in electromagnetic wave path loss prediction and EleDH‐producing systems. However, producing high‐resolution EleDH is challenging because of the massive details in the EleDH data. Direct and high‐time refinement procedures mostly lead to unrealistic outcomes. The study provides a Dense‐Linear convolutional neural network (DLCNN)‐based EleDH refinement technique based on the development of statistical downscaling and super‐resolution technologies. Additionally, the stack approach is used, and the refining order is taken into consideration to ensure precision in high‐time refinement and provide reliable outcomes. To demonstrate the strength of DLCNN in capturing complex internal characteristics of EleDH, a new EleD data set is first funded, which only contains the duct height. From this data set, we use the duct height as the core refinement of the EleD's trapped layer and the thickness of the trapped layer to ensure reliable duct height. Seven super‐resolution models are utilized for fair comparisons. The experimental results prove that the DLCNN has the highest refinement performance; also, it obtained excellent generalization capacity, where the minimum and maximum obtained Accuracy(20%), MAE, and RMSE were 85.22% ∓ 88.30%, 36.09 ∓ 45.97 and 8.68 ∓ 10.14, respectively. High‐resolution EleDH improves path loss prediction, where the minimum and maximum obtained bias were 2.37 ∓ 9.51 dB.

Statistical downscaling of future temperature and precipitation projections in Iraq under climate change scenarios

The research was aimed to; (i) study the observed climate in Iraq from 1971 to 2021 for annually, monthly and seasonally averages of temperature and precipitation for nine meteorological stations; ii) produce RCP4.5 and RCP8.5 scenarios for Iraq region under historical (1900–2005) and future periods (2006–2100) for temperature and precipitation using NCAR-CCSM4 database; and iii) examine the anomaly for near future (2025–2034) and medium future (2050–2059), related to 1986–2005. To evaluate the performance of the monthly averages of temperature and precipitation, three bias correction techniques i.e., Delta, Empirical Quantile Mapping, and Quantile Mapping were applied to nine metrological stations. The results revealed that the Delta is the most efficient method to bias correction for the monthly averages related to temperature and precipitation. The annual average temperature increased from 22.9 C° in 2006, based on both RCP4.5 and RCP8.5, to 24.0 C° and 27.2 C°, respectively, and the anomaly increased from −0.9 C° and −1.9 C° in 2006, to 0.6 C° and 2.4 C°. The precipitation annual and winter month's averages under RCP4.5 and RCP8.5 from 2006 to 2100, 2020–2039 and 2040–2059 showed that southwest region in Iraq (Basra, Missan and Thi-Qar provinces) had more drought and low precipitation than the other regions. From 2029 to 2034 and 2050–2054, rainy years have been predicted, using RCP4.5. Based on RCP.8.5, from 2025 to 2034 and 2050–2055, should witness rainy years, followed by a drought period. The results expected to support decision-makers in incorporating a national initiative to break down social barriers, encourage growth prospects, particularly in southern Iraq.

Climate variability and change in Ecuador: dynamic downscaling of regional projections with RegCM4 and HadGEM2-ES for informed adaptation strategies

Ecuador, a country with distinct coastal (CO), highland (HL), and Amazon (AM) regions that are characterized by unique climatic, ecological, and socio-economic features is highly vulnerable to climate change. This study focuses on these three regions, highlighting their individual importance in the broader context of Ecuador's climate vulnerability. Utilizing dynamically downscaled data from the Regional Climate Model (RCM), we generated precipitation and air temperature projections for the period 2070–2099 under three different climate change scenarios. We indicate projected temperature increases across all three regions: mean temperature increases for the CO, HL and AM regions are of 1.35, 1.55, and 1.21°C, respectively. Each year, the largest temperature increases are predicted for the third quarter (June–August), with the smallest increases predicted for the last quarter (December–February). Precipitation patterns show varied changes, with CO exhibiting a positive mean daily change, in contrast to a mean negative change in the AM region. These region-specific projections underscore the differential impacts of climate change within Ecuador and highlight the necessity for tailored adaptation measures. The study's novel approach, focusing on distinct regional impacts within a single nation, offers valuable insights for policymakers, aiding in the development of effective, region-specific climate change mitigation and adaptation strategies. This targeted approach is crucial to address unique challenges faced by different regions, thereby supporting national resilience strategies.

Statistical downscaling of precipitation in northwestern Iran using a hybrid model of discrete wavelet transform, artificial neural networks, and quantile mapping

Downscaling of daily precipitation from Global Circulation Models (GCMs)-predictors at a station level, especially in arid and semi-arid regions, has remained a formidable challenge yet. The current study aims at proposing a coupled model of Discrete Wavelet Transform (DWT), Artificial Neural Networks (ANNs), and Quantile Mapping (QM) for statistical downscaling of daily precipitation. Given the historic (1978–2005) and future (2006–2100) predictors of eight-selected GCMs under Representative Concentration Pathways (RCPs) 2.6, 4.5, and 8.5, a viable DWT-ANN model was developed for each station. Subsequently, we linked QM to DWT-ANN for bias correction and drizzle effect postprocessing of the DWT-ANN-historic/future projected precipitation. The skill of DWT-ANN-QM was demonstrated using various evaluation metrics, including Taylor diagram, Quantile–Quantile plot, Empirical Cumulative Distribution Function, and wet/dry spell analysis. We appraise the efficacy of the coupled model at 12 weather stations over the Gharehsoo River Basin (GRB) in northwestern Iran. Compared to the observed wet/dry spells, the dry-spells were better simulated via DWT-ANN-QM rather than the wet-spells wherein length and exceedance probability of the spells were overestimated. Results indicated that the future precipitation across the GRB will rise, on average, from 10 to 17% depending on weather station. Seasonal spatial distribution of the middle future (2041–2070) precipitation illustrated that an increase for fall and winter, especially, is expected, whereas the amount of the future spring and summer precipitation is projected to be declined. Having been developed and tested in a semi-arid basin, the efficacy of the coupled model should be further assessed in more humid climates.

Is Bias Correction in Dynamical Downscaling Defensible?

AbstractLocalized projections of 21st‐century hydroclimate variables obtained from downscaling Global Climate Model (GCM) output are central to informing regional impact assessments and infrastructure planning. Regional GCM biases can be significant and, for dynamical downscaling, can be addressed either before (a priori) or after (a posteriori) downscaling. However, a priori bias correction (APBC) has generally unexplored effects on climate change signals. Here we analyze dynamically downscaled solutions of CMIP6 GCMs over the Western U.S., with and without APBC, and quantify APBC's impact on climate change signals relative to other irreducible uncertainty sources. For temperature and precipitation, the uncertainty introduced by APBC is negligible compared to that arising from GCM choice or internal variability. Furthermore, APBC greatly reduces regional models' unrealistically high snow‐water‐equivalent (SWE) biases that result directly from GCM errors. We leverage this finding to encourage the dynamical downscaling community to adopt APBC as a standard operating procedure.

Evaluation of WRF model configurations for dynamic downscaling of tropical cyclones activity over the North Atlantic basin for Lagrangian moisture tracking analysis in future climate

This study assessed five well-established physics suites of the Weather Research and Forecasting (WRF) model in operational forecasting systems in the North Atlantic (NATL) basin or from previous sensitive experiments for dynamic downscaling tropical cyclone (TC) activity. We performed long-term simulations for the 2020 TC season in the NATL and compared the WRF tracks against the HURDAT2 dataset from the US National Hurricane Center. Among the tested configurations, the analysis revealed that the Kain-Fritsch, Purdue Lin, BouLac and revised MM5 schemes for cumulus, microphysics, planetary boundary layer and surface layer, respectively (hereafter WT), outperformed all four others in terms of TC frequency, track density and intensity and showed good performance in the cyclone accumulated energy and TC landfalling locations. In addition, WT well-captured the spatial distribution of accumulated TC precipitation and moisture uptake patterns, although it overestimated the precipitation maxima. Likewise, it agreed on the relative moisture contribution from fixed moisture sources (i.e., Gulf of Mexico, Caribbean Sea, tropical NATL, and western NATL) in the NATL basin. Overall, this study highlighted the high potential of using the WT physics suite in WRF for downscaling TC activity over the NATL basin, which will be useful for TC Lagrangian moisture sources analysis in future climate.

Projected changes in wind erosion climatic erosivity over high mountain Asia: results from dynamical downscaling outputs

Projected changes in wind erosion climatic erosivity over high mountain Asia: results from dynamical downscaling outputs

Mapping Blue Economy Potential Using Spatial Statistical Downscaling Model: Analysis of the Impact of Climate Change on Freshwater Fish Resources

Blue Economy is a sustainable economic concept that focuses on utilizing economic resources in marine, coastal and land ecosystems. Sustainable use of freshwater resources in the blue economy for inland waters supports economic growth with environmental balance. The Statistical Downscaling method is used to understand the impact of climate change on freshwater fish resources. To carry out mapping of the potential of the blue economy, it is carried out by statistical downscaling modeling with satellite variables with Integrated Nested Laplace Approximation parameter estimates. The response variable is satellite variables in the form of average rainfall. The modeling results show that Kalipuro District has the highest blue economy potential, while Kalibaru has the lowest. From the research results, that satellite data on average rainfall is a strong basis for printing statistical downscaling, increasing efficiency with open source digital data sources. Satellite data integration, maximizing analysis and comprehensive blue economy potential efficiency.

Assessment of dynamical downscaling performance over cordex east Asia using MPAS-A global variable resolution model: climatology, seasonal cycle, and extreme events

Assessment of dynamical downscaling performance over cordex east Asia using MPAS-A global variable resolution model: climatology, seasonal cycle, and extreme events

Downscaling Future Precipitation over Mi Oya River Basin using Artificial Neural Networks

Studying future precipitation behaviour in river basins is essential for proper water resources and land-use planning within them, as this will help to reduce the risk and mitigate disasters that can occur in the future. General Circulation Models (GCMs) are used to study future precipitation fluctuations, which simulate large-scale climate variations under the effect of greenhouse gas changes. The GCM runs at a coarse spatial resolution which cannot be directly used for climate impact studies. Therefore, downscaling is required to extract the sub-grid and local scale information. This study examines the use of the Long Short-Term Memory (LSTM) neural network for climate downscaling to the Mi-Oya river basin in Sri Lanka using CNRM-CM5 and HadCM3 GCMs and observed annual data for 35 years. The precipitation data were extracted to cover Sri Lanka. Current downscaling models mostly use Convolutional Neural Networks (CNNs) to downscale GCMs. Out of 42 GCMs, two appropriate GCMs were chosen using the data analysis tool Data Integration and Analysis System (DIAS). The best predictor variables were chosen using the LASSO regression method. In this research, Machine Learning models were implemented using the Google TensorFlow platform. The Nash–Sutcliffe coefficient, Pearson correlation coefficient, and root-mean-square error performance indices were used to evaluate the performances of different downscaling models. Statistical downscaling was performed on the data at RCP 2.6, 4.5, and 8.5 using a LSTM. Subsequently, the changes that would take place by the year 2100 were analysed. The results show that precipitation will be reduced in the 2nd and 3rd decades of the 21st century, and precipitation will increase toward the 22nd century.

Inter‐comparison and validation of high‐resolution surface air temperature reanalysis fields over Italy

AbstractSurface air temperature (t2m) data are essential for understanding climate dynamics and assessing the impacts of climate change. Reanalysis products, which combine observations with retrospective short‐range weather forecasts, can provide consistent and comprehensive datasets. ERA5 represents the state‐of‐the‐art in global reanalyses and supplies initial and boundary conditions for higher‐resolution regional reanalyses designed to capture finer‐scale atmospheric processes. However, these products require validation, especially in complex terrains like Italy. This study analyses the capability of different reanalysis products to reproduce t2m fields over Italy during the 1991–2020 period. The analyses encompass ERA5, ERA5‐Land, the MEteorological Reanalysis Italian DAtaset (MERIDA), the Copernicus European Regional ReAnalysis (CERRA), and the Very High‐Resolution dynamical downscaling of ERA5 REAnalysis over ITaly (VHR‐REA_IT). The validation we conduct pertains to both the spatial distribution of 30‐year seasonal and annual normal values and the daily anomaly records. Each reanalysis is compared with observations projected onto its respective grid positions and elevations, overcoming any model bias resulting from an inaccurate representation of the real topography. Key findings reveal that normal values in reanalyses closely match observational values, with deviations typically below 1°C. However, in the Alps, winter cold biases sometimes exceed 3°C and show a relation with the elevation. Similar deviations occur in the Apennines, Sicily, and Sardinia. Conversely, VHR‐REA_IT shows a warm bias in the Po Valley up to 3°C in summer. Daily anomalies generally exhibit lower errors, with MERIDA showing the highest accuracy and correlation with observational fields. Moreover, when aggregating daily anomalies to annual time scales, the errors in the anomaly records rapidly decrease to <0.5°C. The results of this study empower reanalysis users across multiple sectors to gain a more profound insight into the capabilities and constraints of different reanalysis products. The knowledge and the characterization of the reanalyses t2m bias against observations can indeed be crucial when incorporating these products into their research and practical applications.

Medium-term forecasting of global horizontal solar radiation in Brazil using machine learning-based methods

The generation of electric energy through renewable sources, such as solar photovoltaic (PV) systems, has emerged as one solution to the climate change crisis. To avoid fluctuations in energy generation on the electricity grid, reliable and accurate forecasts are required. Statistical downscaling methodologies often rely on data from General Circulation Model (GCM) to provide information on the local level. In contrast to traditional downscaling approaches, the present study focuses on a data-driven approach using the Global Data Assimilation System (GDAS) database in three different ways to feed the machine learning (ML) models, based on boosting and bagging, to provide medium-term Global Horizontal Irradiance (GHI) forecasts, i.e. up to seven days ahead. In addition, feature selection and feature augmentation techniques are also applied. The results showed that Extra Trees method outperformed the other tested in the present study, with a RMSE of 99.6 and 137.6 W/m2, and correlation 0.31 and 0.52 for June and October, respectively. Thus far, this work marks the initial suggestion of incorporating GDAS analysis data into a data-driven forecasting approach employing bagging and boosting models. This innovative approach demonstrates the potential for integrating global-scale data assimilation systems with local measurements using ML techniques. The outcomes of this research contribute to advancing the field of traditional downscaling methods and provides more robust and specialized forecasting.

Spatiotemporal variation in rice water requirements and area in the cold rice cultivation region of China: Past and Future

Studying rice water requirements (ETc) and area is crucial for developing irrigation schedules and adjusting crop structures. The cold rice cultivation region is China’s main high-quality rice production base and has the lowest temperature, highest latitude, and greatest sensitivity to climate. However, little research has focused on its past rice distributions and ETc, and its future variations are not yet clear. Past ETc was calculated by the Penman–Monteith and crop-corrected coefficient method, and past rice distribution patterns were analyzed based on the annual paddy rice planting area datasets. Future reference crop evapotranspiration (ET0) was projected by the statistical downscaling model (SDSM) based on the CMIP6 ensemble of daily predictor variables. The food and agriculture projections to 2050 datasets were used to project future rice areas, and the patch-generating land use simulation (PLUS) model simulated rice distribution. Finally, we projected future rice water supply demand. The results showed that ETc showed a decreasing trend, and the total rice area and production increased by 4.52 and 5.49 times from 1990 to 2020. Both in the past and future, rice was densely distributed in and expanded into the Sanjiang and Songnen Plains, and the spatial characteristic of ETc was as follows: the west was larger than the east, the north was larger than the south, and the central was in between. In the four future periods, the total ETc demonstrated an upward trend, increasing by 7.44–21.04, 15.64–34.74, 24.74–45.84, and 16.14–41.14 mm under the SSP126, SSP245, SSP370, and SSP585 scenarios, respectively, compared to the historical period. And it is generally ranked as SSP370 > SSP245 > SSP126. Finally, we projected future rice water supply meet the demand caused by rice area expansion.

On Snowmelt Computations in the Climate Change Context: Comparison of Single Site and Multisite Downscaling of Extreme Daily Temperature Processes

This study focuses on how multisite downscaling procedures can provide a significant improvement in the accuracy of impact assessment results as compared to single-site downscaling techniques. The study compared the accuracy of snowmelt computations given by the multisite multivariate statistical downscaling (MMSD) method and the single site downscaling (SDSM) procedure. Results of an illustrative application using daily maximum (Tmax) and minimum (Tmin) temperature series at seven stations in southern Quebec-Ontario region in Canada indicated that MMSD method was able to reproduce accurately the observed spatial and temporal variabilities of daily extreme temperature processes over different locations. MMSD approach preserved more accurately the interstation correlations of daily Tmax (0.78 and 0.7) and Tmin (0.94 and 0.84) respectively for calibration and validation period than SDSM (0.08 and 0.11 for Tmax; and 0.02 for Tmin). MMSD was found capable to produce more accurate snowmelt estimations than those given by the SDSM.

Understanding the Cascade: Removing GCM Biases Improves Dynamically Downscaled Climate Projections

AbstractPolarization surrounding bias correction (BC) in creating climate projections arises from its lack of physicality. Here, we perform and analyze 18 dynamical downscaling simulations (with and without BC) to better understand the physical impacts of BC, applied before downscaling, on regional climate output across the western United States. Without BC, downscaled precipitation is systematically and unrealistically wet biased compared to a hierarchy of observationally based datasets over the 1980–2014 period due to cascading mean‐state Global Climate Model (GCM) biases: (a) overly strong lower‐tropospheric lapse rates (5 K/km), (b) overly cold (2 K) tropospheric temperatures, and (c) anomalous mid‐tropospheric cyclonic vorticity advection. With BC, downscaled precipitation (snow) biases are virtually eliminated (halved). Identified GCM biases are common to the broader Coupled Model Intercomparison Project ensemble. Physical effects of BC on the quality of the regionalized projections, pending an evaluation of BC's distortion of the downscaled climate response, may motivate its broader application by dynamical downscalers.