Tag: Climate Change Downscaling Techniques

Improving the accuracy of precipitation estimates in a typical inland arid region of China using a dynamic Bayesian model averaging approach

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Abstract

Xinjiang Uygur Autonomous Region is a typical inland arid region in China with a sparse and uneven distribution of meteorological stations, limited access to precipitation data, and significant water scarcity. Evaluating and integrating precipitation datasets from different sources to accurately characterize precipitation patterns has become a challenge to provide more accurate and alternative precipitation information for the region, which can even improve the performance of hydrological modelling. This study evaluated the applicability of widely used five satellite-based precipitation products (Climate Hazards Group InfraRed Precipitation with Station (CHIRPS), China Meteorological Forcing Dataset (CMFD), Climate Prediction Center morphing method (CMORPH), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR), and Tropical Rainfall Measuring Mission Multi-satellite Precipitation Analysis (TMPA)) and a reanalysis precipitation dataset (ECMWF Reanalysis v5-Land Dataset (ERA5-Land)) in Xinjiang using ground-based observational precipitation data from a limited number of meteorological stations. Based on this assessment, we proposed a framework that integrated different precipitation datasets with varying spatial resolutions using a dynamic Bayesian model averaging (DBMA) approach, the expectation-maximization method, and the ordinary Kriging interpolation method. The daily precipitation data merged using the DBMA approach exhibits distinct spatiotemporal variability, with an outstanding performance, as indicated by low root mean square error (RMSE=1.40 mm/d) and high Person’s correlation coefficient (CC=0.67). Compared with the traditional simple model averaging (SMA) and individual product data, although the DBMA-fused precipitation data are slightly lower than the best precipitation product (CMFD), the overall performance of DBMA is more robust. The error analysis between DBMA-fused precipitation dataset and the more advanced Integrated Multi-satellite Retrievals for Global Precipitation Measurement Final (IMERG-F) precipitation product, as well as hydrological simulations in the Ebinur Lake Basin, further demonstrate the superior performance of DBMA-fused precipitation dataset in the entire Xinjiang region. Our results showed that the proposed framework for solving the fusion problem of multi-source precipitation data with different spatial resolutions is feasible for application in inland arid regions, and aids in obtaining more accurate regional hydrological information and improving regional water resources management capabilities and meteorological research in these regions.

Improving the accuracy of precipitation estimates in a typical inland arid region of China using a dynamic Bayesian model averaging approach

Latest Results

Abstract

Xinjiang Uygur Autonomous Region is a typical inland arid region in China with a sparse and uneven distribution of meteorological stations, limited access to precipitation data, and significant water scarcity. Evaluating and integrating precipitation datasets from different sources to accurately characterize precipitation patterns has become a challenge to provide more accurate and alternative precipitation information for the region, which can even improve the performance of hydrological modelling. This study evaluated the applicability of widely used five satellite-based precipitation products (Climate Hazards Group InfraRed Precipitation with Station (CHIRPS), China Meteorological Forcing Dataset (CMFD), Climate Prediction Center morphing method (CMORPH), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR), and Tropical Rainfall Measuring Mission Multi-satellite Precipitation Analysis (TMPA)) and a reanalysis precipitation dataset (ECMWF Reanalysis v5-Land Dataset (ERA5-Land)) in Xinjiang using ground-based observational precipitation data from a limited number of meteorological stations. Based on this assessment, we proposed a framework that integrated different precipitation datasets with varying spatial resolutions using a dynamic Bayesian model averaging (DBMA) approach, the expectation-maximization method, and the ordinary Kriging interpolation method. The daily precipitation data merged using the DBMA approach exhibits distinct spatiotemporal variability, with an outstanding performance, as indicated by low root mean square error (RMSE=1.40 mm/d) and high Person’s correlation coefficient (CC=0.67). Compared with the traditional simple model averaging (SMA) and individual product data, although the DBMA-fused precipitation data are slightly lower than the best precipitation product (CMFD), the overall performance of DBMA is more robust. The error analysis between DBMA-fused precipitation dataset and the more advanced Integrated Multi-satellite Retrievals for Global Precipitation Measurement Final (IMERG-F) precipitation product, as well as hydrological simulations in the Ebinur Lake Basin, further demonstrate the superior performance of DBMA-fused precipitation dataset in the entire Xinjiang region. Our results showed that the proposed framework for solving the fusion problem of multi-source precipitation data with different spatial resolutions is feasible for application in inland arid regions, and aids in obtaining more accurate regional hydrological information and improving regional water resources management capabilities and meteorological research in these regions.

Impacts of improved horizontal resolutions in the simulations of mean and extreme precipitation using CMIP6 HighResMIP models over West Africa

Latest Results

Abstract

We conducted an analysis of 16 historical simulations from the High-Resolution Model Intercomparison Project (HighResMIP) as part of the Coupled Model Intercomparison Project (CMIP) phase 6 (CMIP6). These simulations encompass both high- and low-resolution models and aim to investigate the impact of improved horizontal resolution on mean and extreme precipitation in West Africa between 1985 and 2014. Six Expert Team on Climate Change Detection and Indices (ETCCDI) were used to charactererize extreme indices. Bias adjustment was used to detect and adjust the biases in the models. Our observations indicate that the southeastern and southwestern regions of West Africa experience the most significant precipitation, which aligns with the simulations from HighResMIP. The enhanced horizontal resolution notably influences the simulation of orographically induced rainfall in elevated areas and intensifies precipitation in various aspects. When examining the highest 1-day precipitation, our observations reveal that most of the Guinea Coast region had 1-day rainfall exceeding 100 mm. However, this was overestimated and in some simulations underestimated by HighResMIP simulations. Furthermore, an increase in horizontal resolution appears to enhance the ability of high-resolution models to replicate the observed patterns of heavy precipitation (R10mm) and very heavy rainfall (R20mm) days. Spatial and temporal analysis suggests that uncertainty exists in the simulation of extreme precipitation in both high- and low-resolution simulations over West Africa. Also, bias adjustment shows a significant bias in the simulations. To address this issue, we employed a bias adjustment approach.

Impacts of improved horizontal resolutions in the simulations of mean and extreme precipitation using CMIP6 HighResMIP models over West Africa

Latest Results

Abstract

We conducted an analysis of 16 historical simulations from the High-Resolution Model Intercomparison Project (HighResMIP) as part of the Coupled Model Intercomparison Project (CMIP) phase 6 (CMIP6). These simulations encompass both high- and low-resolution models and aim to investigate the impact of improved horizontal resolution on mean and extreme precipitation in West Africa between 1985 and 2014. Six Expert Team on Climate Change Detection and Indices (ETCCDI) were used to charactererize extreme indices. Bias adjustment was used to detect and adjust the biases in the models. Our observations indicate that the southeastern and southwestern regions of West Africa experience the most significant precipitation, which aligns with the simulations from HighResMIP. The enhanced horizontal resolution notably influences the simulation of orographically induced rainfall in elevated areas and intensifies precipitation in various aspects. When examining the highest 1-day precipitation, our observations reveal that most of the Guinea Coast region had 1-day rainfall exceeding 100 mm. However, this was overestimated and in some simulations underestimated by HighResMIP simulations. Furthermore, an increase in horizontal resolution appears to enhance the ability of high-resolution models to replicate the observed patterns of heavy precipitation (R10mm) and very heavy rainfall (R20mm) days. Spatial and temporal analysis suggests that uncertainty exists in the simulation of extreme precipitation in both high- and low-resolution simulations over West Africa. Also, bias adjustment shows a significant bias in the simulations. To address this issue, we employed a bias adjustment approach.

Climate threats to coastal infrastructure and sustainable development outcomes

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Abstract

Climate hazards pose increasing threats to development outcomes across the world’s coastal regions by impacting infrastructure service delivery. Using a high-resolution dataset of 8.2 million households in Bangladesh’s coastal zone, we assess the extent to which infrastructure service disruptions induced by flood, cyclone and erosion hazards can thwart progress towards the Sustainable Development Goals (SDGs). Results show that climate hazards potentially threaten infrastructure service access to all households, with the poorest being disproportionately threatened in 69% of coastal subdistricts. Targeting adaptation to these climatic threats in one-third (33%) of the most vulnerable areas could help to safeguard 50–85% of achieved progress towards SDG 3, 4, 7, 8 and 13 indicators. These findings illustrate the potential of geospatial climate risk analyses, which incorporate direct household exposure and essential service access. Such high-resolution analyses are becoming feasible even in data-scarce parts of the world, helping decision-makers target and prioritize pro-poor development.

Climate threats to coastal infrastructure and sustainable development outcomes

Latest Results

Abstract

Climate hazards pose increasing threats to development outcomes across the world’s coastal regions by impacting infrastructure service delivery. Using a high-resolution dataset of 8.2 million households in Bangladesh’s coastal zone, we assess the extent to which infrastructure service disruptions induced by flood, cyclone and erosion hazards can thwart progress towards the Sustainable Development Goals (SDGs). Results show that climate hazards potentially threaten infrastructure service access to all households, with the poorest being disproportionately threatened in 69% of coastal subdistricts. Targeting adaptation to these climatic threats in one-third (33%) of the most vulnerable areas could help to safeguard 50–85% of achieved progress towards SDG 3, 4, 7, 8 and 13 indicators. These findings illustrate the potential of geospatial climate risk analyses, which incorporate direct household exposure and essential service access. Such high-resolution analyses are becoming feasible even in data-scarce parts of the world, helping decision-makers target and prioritize pro-poor development.

Projection of future precipitation, air temperature, and solar radiation changes in southeastern China

Latest Results

Abstract

Evidence of climate change can be observed in multiple climate variables, including air temperature rises and precipitation pattern changes. To manage water resources and agriculture effectively, it's important to project climate variables' changes at the local level, as these changes can vary depending on the specific area. The baseline weather data trend was analyzed using the percentage change (PC) method and the Innovative Trend Analysis (ITA) technique at three cluster levels: cluster 1 (PC: 0–20%), cluster 2 (PC: 21–80%), and cluster 3 (PC: 81–100%). The precipitation (Prec.), maximum (Tmax), and minimum (Tmin) temperatures showed downward trends in 9, 4, and 6 stations out of 24 stations, respectively. The SDSM model performed best in predicting Prec., while the LARS-WG model was more effective in predicting Tmax, Tmin, and solar radiation (SR). The average monthly Prec. percentage change shows both rising and falling trends in different weather areas for all three time periods (2040, 2060, and 2090) and for both RCPs (RCP4.5 and RCP8.5). In contrast to precipitation, both Tmax and Tmin consistently showed an upward trend across all meteorological stations for both RCPs and three-time frames. Across the four distinct plain regions, the overall projection suggests a slight increase in precipitation. The study predicts the highest increase in precipitation to occur in June across all meteorological stations. Seasonally, the greatest increase in precipitation is projected during summer (JJA) by 5.10%, while the largest decrease is expected during winter (DJF) by 3.29%. Additionally, precipitation variability shows an increase from RCP4.5 to RCP8.5 and from near-term (2040) to long-term (2090), with the northern Jiangsu Plain exhibiting the highest variation. The biggest rise in Tmax/Tmin was observed at RCP 8.5, by 2.69/2.39 °C, and in the long term (2090), by 3.25/2.86 °C. This was compared to RCP 4.5 by 1.73/1.51 °C, in the near term (2040) by 1.24/1.08 °C, and in the mid-term (2060) by 2.14/1.90 °C. The highest increase in Tmax is expected compared to Tmin, leading to the highest diurnal temperature (DTR) at all three periods and both RCPs. Seasonally, the highest increase is projected in the autumn for both Tmax and Tmin. Similar to Tmax and Tmin, the longest time period (2090) exhibits the highest increase in solar radiation, followed by the midterm (2060) and then the short term (2040). Unlike Tmax and Tmin, the highest increase in SR is predicted during the summer season (JJA), while the lowest increase is projected during the winter season (DJF). The future projections highlight the expectation of a wettest and hottest summer, along with the driest and coldest winter. These findings provide valuable insights for water resource planners, agricultural managers, and policymakers, as these climate variables play a significant role in crop production and water allocation decisions.

Projection of future precipitation, air temperature, and solar radiation changes in southeastern China

Latest Results

Abstract

Evidence of climate change can be observed in multiple climate variables, including air temperature rises and precipitation pattern changes. To manage water resources and agriculture effectively, it's important to project climate variables' changes at the local level, as these changes can vary depending on the specific area. The baseline weather data trend was analyzed using the percentage change (PC) method and the Innovative Trend Analysis (ITA) technique at three cluster levels: cluster 1 (PC: 0–20%), cluster 2 (PC: 21–80%), and cluster 3 (PC: 81–100%). The precipitation (Prec.), maximum (Tmax), and minimum (Tmin) temperatures showed downward trends in 9, 4, and 6 stations out of 24 stations, respectively. The SDSM model performed best in predicting Prec., while the LARS-WG model was more effective in predicting Tmax, Tmin, and solar radiation (SR). The average monthly Prec. percentage change shows both rising and falling trends in different weather areas for all three time periods (2040, 2060, and 2090) and for both RCPs (RCP4.5 and RCP8.5). In contrast to precipitation, both Tmax and Tmin consistently showed an upward trend across all meteorological stations for both RCPs and three-time frames. Across the four distinct plain regions, the overall projection suggests a slight increase in precipitation. The study predicts the highest increase in precipitation to occur in June across all meteorological stations. Seasonally, the greatest increase in precipitation is projected during summer (JJA) by 5.10%, while the largest decrease is expected during winter (DJF) by 3.29%. Additionally, precipitation variability shows an increase from RCP4.5 to RCP8.5 and from near-term (2040) to long-term (2090), with the northern Jiangsu Plain exhibiting the highest variation. The biggest rise in Tmax/Tmin was observed at RCP 8.5, by 2.69/2.39 °C, and in the long term (2090), by 3.25/2.86 °C. This was compared to RCP 4.5 by 1.73/1.51 °C, in the near term (2040) by 1.24/1.08 °C, and in the mid-term (2060) by 2.14/1.90 °C. The highest increase in Tmax is expected compared to Tmin, leading to the highest diurnal temperature (DTR) at all three periods and both RCPs. Seasonally, the highest increase is projected in the autumn for both Tmax and Tmin. Similar to Tmax and Tmin, the longest time period (2090) exhibits the highest increase in solar radiation, followed by the midterm (2060) and then the short term (2040). Unlike Tmax and Tmin, the highest increase in SR is predicted during the summer season (JJA), while the lowest increase is projected during the winter season (DJF). The future projections highlight the expectation of a wettest and hottest summer, along with the driest and coldest winter. These findings provide valuable insights for water resource planners, agricultural managers, and policymakers, as these climate variables play a significant role in crop production and water allocation decisions.

Non-crossing Quantile Regression Neural Network as a Calibration Tool for Ensemble Weather Forecasts

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Abstract

Despite the maturity of ensemble numerical weather prediction (NWP), the resulting forecasts are still, more often than not, under-dispersed. As such, forecast calibration tools have become popular. Among those tools, quantile regression (QR) is highly competitive in terms of both flexibility and predictive performance. Nevertheless, a long-standing problem of QR is quantile crossing, which greatly limits the interpretability of QR-calibrated forecasts. On this point, this study proposes a non-crossing quantile regression neural network (NCQRNN), for calibrating ensemble NWP forecasts into a set of reliable quantile forecasts without crossing. The overarching design principle of NCQRNN is to add on top of the conventional QRNN structure another hidden layer, which imposes a non-decreasing mapping between the combined output from nodes of the last hidden layer to the nodes of the output layer, through a triangular weight matrix with positive entries. The empirical part of the work considers a solar irradiance case study, in which four years of ensemble irradiance forecasts at seven locations, issued by the European Centre for Medium-Range Weather Forecasts, are calibrated via NCQRNN, as well as via an eclectic mix of benchmarking models, ranging from the naïve climatology to the state-of-the-art deep-learning and other non-crossing models. Formal and stringent forecast verification suggests that the forecasts post-processed via NCQRNN attain the maximum sharpness subject to calibration, amongst all competitors. Furthermore, the proposed conception to resolve quantile crossing is remarkably simple yet general, and thus has broad applicability as it can be integrated with many shallow- and deep-learning-based neural networks.

Integrated geospatial approach for adaptive rainwater harvesting site selection under the impact of climate change

Latest Results

Abstract

The impact of global climate change on water resources is a pressing concern, particularly in arid and semi-arid regions, where water shortages are becoming increasingly severe. Rainwater harvesting (RWH) offers a promising solution to address these challenges. However, the process of selecting suitable RWH sites is complex. This paper introduces a comprehensive methodology that leverages various technologies and data sources to identify suitable RWH locations in the northern region of Iraq, considering both historical and future scenarios. The study employs remote sensing and geographic information systems to collect and process geospatial data, which are essential for the site selection process. AHP is utilized as a decision-making tool to assess and rank potential RWH locations based on multiple criteria, helping to prioritize the most suitable sites. The WLC approach is used to combine and weigh various factors, enabling a systematic evaluation of site suitability. To account for the uncertainty associated with future climate conditions, a stochastic weather generator is employed to simulate historical and future precipitation data for period (1980–2022) and (2031–2100). This ensures that the assessment considers changing climate patterns. Historical precipitation values ranged from 270 to 490 mm, while future projections indicate a decrease, with values varying from 255 to 390 mm. This suggests a potential reduction in available water resources due to climate change. The runoff for historical rainfall values ranged from 190 mm (poor) to 490 mm (very good). In the future projections, runoff values vary from 180 mm (very poor) to 390 mm (good). This analysis highlights the potential impact of reduced precipitation on water availability. There is a strong correlation between rainfall and runoff, with values of 95% for historical data and 98.83% for future projections. This indicates that changes in precipitation directly affect water runoff. The study incorporates several criteria in the model, including soil texture, historical and future rainfall data, land use/cover, slope, and drainage density. These criteria were selected based on the nature of the study region and dataset availability. The suitability zones are classified into four categories for both historical potential and future projections of RWH zones: very high suitability, covering approximately 8.2%. High suitability, encompassing around 22.6%. Moderate suitability, constituting about 37.4%. Low suitability, accounting for 31.8% of the study region. For the potential zones of RWH in the future projection, the distribution is as follows: very high suitability, approximately 6.1%. High suitability, around 18.3%. Moderate suitability, roughly 31.2%. Low suitability, making up about 44.4% of the study region. The research's findings have significant implications for sustainable water resource management in the northern region of Iraq. As climate change exacerbates water scarcity, identifying suitable RWH locations becomes crucial for ensuring water availability. This methodology, incorporating advanced technology and data sources, provides a valuable tool for addressing these challenges and enhancing the future of water management to face of climate change. However, more investigations and studies need to be conducted in near future in the study region.