Daily Bias Correction Research Articles

Evaluation of future climatology and its uncertainty under SSP scenarios based on a bias processing procedure: A case study of the Lancang-Mekong River Basin

The Lancang-Mekong River is the most important international river in Southeast Asia, and its water resources are invaluable for the survival of the inhabitants of the basin, while the climatic conditions have a significant impact on the production and livelihoods of the riparian countries. In this study, future climate change in the Lancang-Mekong River Basin (LMRB) was projected based on precipitation and temperature from CMIP6. First, a bias processing procedure was established to correct the initial bias of climate models using the Daily Bias Correction (DBC) method, and the performance of equal weighted averaging (EW) and Bayesian mode averaging (BMA) of multi-model ensembles was compared. The characteristics of future precipitation and temperature changes were projected in terms of mean climatology, seasonal variation and extreme events, respectively, from which the future trends of the four weather extremes (dry-cold, dry-hot, wet-cold and wet-hot) were projected. Evaluation based on mean climatology, daily frequency distribution, temporal change and its bias showed that the climate bias correction procedure has good performance. It was found that future precipitation in the LMRB will increase by 50–120 mm in the near future (2031–2050) and 150–400 mm in the far future (2061–2090), with higher increases in the upper Mekong River Basin (MRB), Lower MRB and Mekong Delta than in the Lancang River Basin (LRB) and middle MRB. Future increase in precipitation will occur mainly from June to October, while April and May will see a decrease by 10–30 mm, and the number of heavy precipitation days (R25, daily precipitation exceeding 25 mm) will increase by about 5 days in the eastern part of the lower and middle MRB. The mean temperature will increase by 1–3 °C in most parts of the LMRB, with the LRB experiencing an increase of about 3 °C higher than in the MRB. Future maximum and minimum daily temperatures will increase by 1–2 °C in the near future and by 2–5 °C in the far future. Dry-hot days will increase by about 20 days in the lower and middle MRB, and wet-hot days will increase significantly in the MRB, by 30–80 days in the near future and by 50–100 days in the far future.

Spatiotemporal Projections of Precipitation in the Lancang–Mekong River Basin Based on CMIP6 Models

The Lancang–Mekong River Basin (LMRB) is the largest international river in Southeast Asia, supporting a population of about 70 million people. Precipitation is the main source of water resources in the basin, with significant impacts on ecology, production, and livelihoods in the basin. In this study, future precipitation was projected using the Coupled Model Intercomparison Project Phase 6 (CMIP6) climate models. The initial bias of each model was corrected using the daily bias-correction (DBC) method, and then the models were ensembled using the Bayesian model-averaging (BMA) method. The evaluation, based on metrics such as climatology bias, root-mean-square error (RMSE), mean absolute error (MAE), and correlation coefficient (COR), showed that the ensemble precipitation performs better than the individual models. Precipitation under four future Shared Socioeconomic Pathway scenarios (SSP126, SSP245, SSP370, SSP585) displayed an increasing trend throughout the LMRB. The anomalies in annual precipitation in 2061–2090 under each scenario are 136 mm, 142 mm, 114 mm, and 227 mm, in that order. Precipitation in spring and winter shows a trend of increasing in the northern LMRB and decreasing in the southern LMRB, and precipitation in summer and autumn shows a significant trend of increasing in almost the whole basin (significance level 0.05). Spring precipitation in the Mekong Delta decreases in all scenarios. The ratio of wet-season precipitation to dry-season precipitation shows an increasing trend for all scenarios, indicating that the difference between wet-season precipitation and dry-season precipitation will increase in the future. For daily precipitation, the Lancang River Basin (LRB) is dominated by a 3–5% increase in the number of days with 5–10 mm/d of precipitation and the Mekong River Basin (MRB) by a 3–5% increase in the number of days with 10–20 mm/d of precipitation under four SSP scenarios in 2061–2090. There are important changes in the spatial distribution of future precipitation, with the 2500 mm isohyet expanding outwards in a circular pattern and the center of the 1500 mm isohyet moving westwards; i.e., areas with annual precipitation exceeding 2500 mm and 1500 mm will expand. For dry-season precipitation, the 500 mm isohyet shrinks, mainly in a circular pattern towards the center, while the 300 mm isohyet moves mainly towards the east, indicating that areas of dry-season precipitation below 500 mm and 300 mm will expand. In the future, the LMRB will generally become wetter in the wet season and drier in the dry season.

Projecting Hydrological Responses to Climate Change Using CMIP6 Climate Scenarios for the Upper Huai River Basin, China

The influence of climate change on the regional hydrological cycle has been an international scientific issue that has attracted more attention in recent decades due to its huge effects on drought and flood. It is essential to investigate the change of regional hydrological characteristics in the context of global warming for developing flood mitigation and water utilization strategies in the future. The purpose of this study is to carry out a comprehensive analysis of changes in future runoff and flood for the upper Huai River basin by combining future climate scenarios, hydrological model, and flood frequency analysis. The daily bias correction (DBC) statistical downscaling method is used to downscale the global climate model (GCM) outputs from the sixth phase of the Coupled Model Intercomparison Project (CMIP6) and to generate future daily temperature and precipitation series. The Xinanjiang (XAJ) hydrological model is driven to project changes in future seasonal runoff under SSP245 and SSP585 scenarios for two future periods: 2050s (2031–2060) and 2080s (2071–2100) based on model calibration and validation. Finally, the peaks over threshold (POT) method and generalized Pareto (GP) distribution are combined to evaluate the changes of flood frequency for the upper Huai River basin. The results show that 1) GCMs project that there has been an insignificant increasing trend in future precipitation series, while an obvious increasing trend is detected in future temperature series; 2) average monthly runoffs in low-flow season have seen decreasing trends under SSP245 and SSP585 scenarios during the 2050s, while there has been an obvious increasing trend of average monthly runoff in high-flow season during the 2080s; 3) there is a decreasing trend in design floods below the 50-year return period under two future scenarios during the 2050s, while there has been an significant increasing trend in design flood during the 2080s in most cases and the amplitude of increase becomes larger for a larger return period. The study suggests that future flood will probably occur more frequently and an urgent need to develop appropriate adaptation measures to increase social resilience to warming climate over the upper Huai River basin.

Thermodynamic response of precipitation extremes to climate change and its impacts on floods over China

<p indent=0mm>As the planet warms, shifts in the thermodynamic and dynamic environments of the climate system exert substantial impacts on the energy budget and water cycle. Understanding the response of precipitation extremes to climate change is essential for mitigating weather-related hazards and facilitating adaptive water resources management. The moisture holding capacity of the atmosphere, governed by the Clausius-Clapeyron (C-C) relationship, increases with temperature at a rate of about 7%/°C. Thermodynamically, precipitation intensity is expected to increase exponentially and worsen flood conditions with atmospheric warming. However, both observational and satellite data have revealed a nonmonotonic (hook) scaling of precipitation and storm runoff, in which extremes strengthen with warming temperature up to a peak point (<italic>T</italic>_pp) and decline thereafter. Few studies have yet considered whether and why the hook structure might change in a future warming climate. Moreover, existing studies have largely failed to detect and attribute changes of extreme precipitation to atmospheric thermodynamics versus dynamics, limiting reliable projection of future hydrological hazards and climate simulations. Here, we build upon existing studies to understand how precipitation and storm runoff scaling might change over Chinese mainland. We use gridded observational data to examine the temperature scaling of precipitation and storm runoff extremes, extending the C-C relationship. Using the ERA5 reanalysis dataset, we diagnose the impacts of total column water vapor (TCWV), convective available potential energy (CAPE) and relative humidity on precipitation extremes. After partitioning the thermodynamic and dynamic contributions of precipitation extremes based on the energy budget and water vapor balance, we quantify the underlying physical mechanisms of hook structures. Future climate scenarios are projected by using a large ensemble from 21 global climate models (GCMs) within Coupled Model Inter-comparison Project Phase-6 (CMIP6). Future streamflow series of 151 basins are projected by forcing hydrological model with bias-corrected climate model outputs. We quantify changes in the hook structures and their <italic>T</italic>_pp, and investigate implications of these shifts for future storms and flooding. The results show that relative humidity usually decreases with warming near-surface temperatures over most areas of China, while the TCWV and CAPE exhibit positive scaling rates with temperatures. The ERA5 reanalysis data over China reveals that atmospheric dynamics are responsible for the intensification of precipitation extremes and even flooding under very warm temperatures. The daily bias correction method reduces the systematical biases of GCMs outputs adequately, and captures extreme precipitation and temperatures well. By using four different hydrological models, we find that the Xinanjiang model typically exhibits the best performance in simulating daily streamflow over Chinese mainland. Projections of future hydro-climatological scenarios indicate that the hook structures do not set a firm upper-bound constraint on the intensification of extremes; rather, both the hook structure and the <italic>T</italic>_pp shift toward warmer temperatures in a warming climate. Alongside the shifts in <italic>T</italic>_pp, future storm and flooding extremes are projected to intensify by 20%−30%. These findings highlight the urgency in enhancing societal resilience to climate change, and understanding how the intensification of weather-related hazards might affect existing infrastructure and ecosystems.

Adaptive optimal allocation of water resources response to future water availability and water demand in the Han River basin, China

Global warming and anthropogenic changes can result in the heterogeneity of water availability in the spatiotemporal scale, which will further affect the allocation of water resources. A lot of researches have been devoted to examining the responses of water availability to global warming while neglected future anthropogenic changes. What’s more, only a few studies have investigated the response of optimal allocation of water resources to the projected climate and anthropogenic changes. In this study, a cascade model chain is developed to evaluate the impacts of projected climate change and human activities on optimal allocation of water resources. Firstly, a large set of global climate models (GCMs) associated with the Daily Bias Correction (DBC) method are employed to project future climate scenarios, while the Cellular Automaton–Markov (CA–Markov) model is used to project future Land Use/Cover Change (LUCC) scenarios. Then the runoff simulation is based on the Soil and Water Assessment Tool (SWAT) hydrological model with necessary inputs under the future conditions. Finally, the optimal water resources allocation model is established based on the evaluation of water supply and water demand. The Han River basin in China was selected as a case study. The results show that: (1) the annual runoff indicates an increasing trend in the future in contrast with the base period, while the ascending rate of the basin under RCP 4.5 is 4.47%; (2) a nonlinear relationship has been identified between the optimal allocation of water resources and water availability, while a linear association exists between the former and water demand; (3) increased water supply are needed in the water donor area, the middle and lower reaches should be supplemented with 4.495 billion m3 water in 2030. This study provides an example of a management template for guiding the allocation of water resources, and improves understandings of the assessments of water availability and demand at a regional or national scale.

Does the Hook Structure Constrain Future Flood Intensification Under Anthropogenic Climate Warming?

AbstractAtmospheric moisture holding capacity increases with temperature by about 7% per °C according to the Clausius‐Clapeyron relationship. Thermodynamically then, precipitation intensity should exponentially intensify and thus worsen flood conditions as the climate warms. However, regional and global analyses often report a nonmonotonic (hook) scaling of precipitation and runoff, in which extremes strengthen with rising temperature up to a maximum or peak point (Tpp) and decline thereafter. The underlying cause of this hook structure is not yet well‐understood, and whether it may shift and/or regulate storm runoff extremes under anthropogenic climate warming remains unknown. Here, we examine temperature scaling of precipitation and storm runoff extremes under different climate conditions using observations and large ensemble hydroclimatic simulations over mainland China. In situ observations suggest a spatially homogeneous, negative response of relative humidity to warming climates over 34.6% of the land area, and the remaining hook‐dominated regions usually show a colder Tpp than that of precipitation or storm runoff extremes. The precipitation and streamflow series over mainland China's catchments throughout the 21st century are projected by a model cascade chain under a high‐end emission scenario (RCP 8.5), which involves 31 CMIP5 climate models, 11 CMIP6 climate members, a daily bias correction method, and four lumped conceptual hydrological models. The CMIP5 ensemble projects that the hook structures shift toward warmer temperature bins, resulting in 10%–30% increases in storm runoff extremes over mainland China, while the CMIP6 ensemble projects more severe flood conditions in future warming climates.

Analysis of the Evolution of Drought, Flood, and Drought-Flood Abrupt Alternation Events under Climate Change Using the Daily SWAP Index

With the increase of drought and flood frequency, the drought-flood abrupt alternation events occur frequently. Due to the coexistence and rapid transformation of drought and flood, the drought-flood abrupt alternation events is often more harmful and threatening than the single drought or flood event to the security of the society. This study is to synthetically evaluate the evolving characteristics of drought, flood, and drought-flood abrupt alternation events under climate change, which are identified by using the Standard Weighted Average Precipitation (SWAP) index. The variability of drought, flood, and drought-flood abrupt alternation events in the future is predicted by using GCM projections, whose outputs are corrected by using a daily bias correction method. The results show that: (1) The SWAP index has the capability to judge reliably the onset, duration, and intensity over the study areas, and can be used to monitor drought-flood abrupt alternation events efficiently; (2) In the reference period (1961–2005), for the drought-flood abrupt alternation events, the frequency has a downward trend in the upper reaches and an upward trend in the lower reaches, and the spatial distribution of intensity shows a contrary law to that of frequency; (3) The frequency and intensity of drought-flood abrupt alternation events show an upward trend in the whole basin in the future period (2021–2095), under the RCP4.5 and RCP8.5 scenarios. These results indicate that drought-flood abrupt alternation events can be more frequent, and the intensity will significantly increase in the 21st century, which may likely pose a serious impact on this basin.

Twenty-first-century glacio-hydrological changes in the Himalayan headwater Beas River basin

Abstract. The Himalayan Mountains are the source region of one of the world's largest supplies of freshwater. The changes in glacier melt may lead to droughts as well as floods in the Himalayan basins, which are vulnerable to hydrological changes. This study used an integrated glacio-hydrological model, the Glacier and Snow Melt – WASMOD model (GSM-WASMOD), for hydrological projections under 21st century climate change by two ensembles of four global climate models (GCMs) under two Representative Concentration Pathways (RCP4.5 and RCP8.5) and two bias-correction methods (i.e., the daily bias correction (DBC) and the local intensity scaling (LOCI)) in order to assess the future hydrological changes in the Himalayan Beas basin up to Pandoh Dam (upper Beas basin). Besides, the glacier extent loss during the 21st century was also investigated as part of the glacio-hydrological modeling as an ensemble simulation. In addition, a high-resolution WRF precipitation dataset suggested much heavier winter precipitation over the high-altitude ungauged area, which was used for precipitation correction in the study. The glacio-hydrological modeling shows that the glacier ablation accounted for about 5 % of the annual total runoff during 1986–2004 in this area. Under climate change, the temperature will increase by 1.8–2.8 ∘C at the middle of the century (2046–2065), and by 2.3–5.4 ∘C until the end of the century (2080–2099). It is very likely that the upper Beas basin will get warmer and wetter compared to the historical period. In this study, the glacier extent in the upper Beas basin is projected to decrease over the range of 63 %–87 % by the middle of the century and 89 %–100 % at the end of the century compared to the glacier extent in 2005. This loss in glacier area will in general result in a reduction in glacier discharge in the future, while the future streamflow is most likely to have a slight increase because of the increase in both precipitation and temperature under all the scenarios. However, there is widespread uncertainty regarding the changes in total discharge in the future, including the seasonality and magnitude. In general, the largest increase in river total discharge also has the largest spread. The uncertainty in future hydrological change is not only from GCMs, but also from the bias-correction methods and hydrological modeling. A decrease in discharge is found in July from DBC, while it is opposite for LOCI. Besides, there is a decrease in evaporation in September from DBC, which cannot be seen from LOCI. The study helps to understand the hydrological impacts of climate change in northern India and contributes to stakeholder and policymaker engagement in the management of future water resources in northern India.

A copula-based analysis of projected climate changes to bivariate flood quantiles

Climate change will lead to great impacts on flood frequency curve and design floods in the future. However, traditional hydrologic approaches often fail to analyze the flood characteristics within a bivariate framework under changing environment. Moreover, previous studies investigating bivariate characteristics of flood usually do not derive the adaptive flood quantiles. This study assesses the implications of climate change for future bivariate quantiles of flood peak and volume in Ganjiang River basin, China. The outputs of two global climate models (BNU-ESM and BCC-CSM1.1) are statistically downscaled by Daily bias correction (DBC) method and used as inputs of the Xinanjiang hydrological model to simulate streamflow during 1966–2099. Projections for future flood (2020–2099) under Representative Concentration Pathway (RCP) 8.5 scenario are divided into two 40-year horizons (2040s, 2080s) and a comparison is made between these time horizons and the baseline (1966–2005). Univariate flood frequency analysis indicates that there is a considerable increase in the magnitude and frequency of flood under the RCP8.5 scenario, especially for the higher return periods. The bivariate quantile curves under different levels of Joint Return Period (JRP) for historical and future periods are derived by copula functions and the most likely realizations are estimated. It is found that climate change has heavier impacts on the future joint bivariate quantiles for larger return periods. Finally the adaptive isolines and most likely flood quantiles under a JRP are derived from analyzing the merged series by non-stationary copula-based models. The results highlight that the joint probability, illustrated by the isoline of a given JRP, varies significantly over time when non-stationary models are applied. This study incorporates the impacts of climate change on bivariate flood quantiles and develops an adaptive quantile estimation approach, which may provide useful information for the references of flood risk assessment and management under changing environment.

Estimating Parameter of Nonlinear Bias Correction Method Using NSGA-II in Daily Precipitation Data

Nonlinear (NL) method is the most effective bias correction method for correcting statistical bias when observation precipitation data can not be approximated using gamma distribution. Since NL method only adjusts mean and variance, it does not perform well in handling bias on quantile values. This paperpresents a scheme of NL method with additional condition aiming to mitigate bias on quantile values. Non-dominated Sorting Genetic Algorithm II (NSGA-II) was applied to estimate parameter of NL method. Furthermore, to investigate suitability of application of NSGA-II, we performed Single Objective Genetic Algorithm (SOGA) as a comparison. The experiment results revealed NSGA-II was suitable when solution of SOGA produced low fitness. Application of NSGA-II could minimize impact of daily bias correction on monthly precipitation. The proposed scheme successfully reduced biases on mean, variance, first and second quantile However, biases on third and fourth moment could not be handled robustly while biases on third quantile only reduced during dry months.

Impact assessment on water resources management under extreme climate change

River flows would be influenced greatly by climate change, which may cause further stress on water resources management by altering the quantity and distribution of runoff. In this paper, taking the Hanjiang River basin for instance, projections of precipitation and temperature are generated from two GCMs under RCP8.5 scenario, an extreme condition. Then the outputs are statistically downscaled and corrected by the daily bias correction method, a hybrid method of combining the daily translation and the local intensity scaling method. The VIC distributed hydrological model is used for the runoff simulation. Results show that the projections of two GCMs consistent with each other. There is a general increase in the annual mean precipitation and temperature in the Hanjiang River Basin in the future period (2021-2099), and the annual mean runoff of the Danjiangkou reservoir increases significantly compared with historical period (1980-2010). However, the annual runoff variability would increase the flood control pressure in wet season, aggravate the conflict between power generation and water supply in dry season despite increasing the water supply capacity in storage season.

Bias corrections of long‐term (1973–2004) daily precipitation data over the northern regions

A consistent daily bias correction procedure was applied at 4802 stations over high latitude regions (North of 45°N) to quantify the precipitation gauge measurement biases of wind‐induced undercatch, wetting losses, and trace amount of precipitation for the last 30 years. These corrections have increased the gauge‐measured monthly precipitation significantly by up to 22 mm for winter months, and slightly by about 5 mm during summer season. Relatively, the correction factors (CF) are small in summer (10%), and very large in winter (80–120%) because of the increased effect of wind on gauge undercatch of snowfall. The CFs also vary over space particularly in snowfall season. Significant CF differences were found across the USA/Canada borders mainly due to differences in catch efficiency between the national gauges. Bias corrections generally enhance monthly precipitation trends by 5–20%. These results point to a need to review our current understanding of the Arctic fresh water budget and its change.

Correction of synoptic precipitation observations due to systematic measuring errors with special regard to precipitation phases

A recently developed method for event-based daily bias-correction of synoptic precipitation observations regarding systematic measuring errors was transfered at the Global Precipitation Climatology Centre (GPCC) from regional to global applications. Using the reported present weather, an analysis based on more than 600 000 global synoptic data from 16 winter months was done, which made it possible to relate air temperature and dew point temperature to the probable distribution of liquid, solid and mixed precipitation phase. Based on this information, synoptic precipitation observations can be corrected regarding systematic measuring errors on a daily resolution, which makes the estimation of extreme precipitation events more reliable.