Tag: Climate Change Downscaling Techniques

Abstract

Northeast China is an important base for grain production, dominated by rain-fed agriculture that relies on green water. However, in the context of global climate change, rising regional temperatures, changing precipitation patterns, and increasing drought frequency pose threats and challenges to agricultural green water security. This study provides a detailed assessment of the spatiotemporal characteristics and development trends of green water security risks in the Northeast region under the base period (2001–2020) and the future (2031–2090) climate change scenarios (SSP245 and SSP585) using the green water scarcity (GWS) index based on raster-scale crop spatial distribution data, Delta downscaling bias-corrected ERA5 data, and CMIP6 multimodal data. During the base period, the green water risk-free zone for dry crops is mainly distributed in the center and east of the Northeast region (72.4% of the total area), the low-risk zone is primarily located in the center (14.0%), and the medium-risk (8.3%) and high-risk (5.3%) zones are mostly in the west. Under SSP245 and SSP585 future climate change scenarios, the green water security risk shows an overall expansion from the west to the center and east, with the low-risk zone increasing to 21.6% and 23.8%, the medium-risk zone increasing to 16.0% and 17.9%, and the high-risk zone increasing to 6.9% and 6.8%, respectively. Considering dry crops with GWS greater than 0.1 as in need of irrigation, the irrigated area increases from 27.6% (base period) to 44.5% (SSP245) and 48.6% (SSP585), with corresponding increases in irrigation water requirement (IWR) of 4.64 and 5.92 billion m³, respectively, which further exacerbates conflicts between supply and demand of agricultural water resources. In response to agricultural green water security risks, coping strategies such as evapotranspiration (ET)-based water resource management for dry crops and deficit irrigation are proposed. The results of this study can provide scientific basis and decision support for the development of Northeast irrigated agriculture and the construction planning of the national water network.

Abstract

In 2022, a catastrophic flood triggered by the extreme precipitation in Sind Province, Pakistan. To better understand the comprehensive response of water vapor, rainfall, topography, and flood, the source of water vapor for the flood was calculated by the NCAR Command Language (NCL) application. Simultaneously, the Global Precipitation Measurement (GPM) data was collected from NASA for overlay analysis with water vapor observations. In addition, a digital elevation model (DEM) was also obtained to analyze the impact of topography on flood inundation. Importantly, multi Sentinel-1 data was used to monitor the long-term changes in flood inundation area. The extreme precipitation is dominated by water vapor continue transferred by southwest monsoon, especially impacted by the occurrence of cyclone. Simultaneously, influenced by the steep terrain that located in the north and west of Pakistan, the extreme precipitation first occurred in Islamabad and its adjacent area, subsequently in Punjab Province, and finally concentrated in Sind Province. The surface runoff induced by rainstorm converged in the junction of Sind and Punjab Province with the pattern of fire hose effect. Subsequently, the flood in Indus River in the Sind Province overflow into the low-lying area along the bank of Indus River due to the terrain of Indus River in these regions has the characteristics of over ground river, and the flood flow capacity is lower than that in northern of Pakistan. In addition, the long-term changes in the flood inundation area can be summarized into four stages: increase slowly period (In June), increase slightly period (In July), increase rapidly period (Between August and the beginning of September), rapidly decline period (After September 15, 2022). Importantly, a conceptual model of disaster caused by the fire pipe effect is summarized based on the comprehensive response of water vapor, rainfall, and topography.

Abstract

In 2022, a catastrophic flood triggered by the extreme precipitation in Sind Province, Pakistan. To better understand the comprehensive response of water vapor, rainfall, topography, and flood, the source of water vapor for the flood was calculated by the NCAR Command Language (NCL) application. Simultaneously, the Global Precipitation Measurement (GPM) data was collected from NASA for overlay analysis with water vapor observations. In addition, a digital elevation model (DEM) was also obtained to analyze the impact of topography on flood inundation. Importantly, multi Sentinel-1 data was used to monitor the long-term changes in flood inundation area. The extreme precipitation is dominated by water vapor continue transferred by southwest monsoon, especially impacted by the occurrence of cyclone. Simultaneously, influenced by the steep terrain that located in the north and west of Pakistan, the extreme precipitation first occurred in Islamabad and its adjacent area, subsequently in Punjab Province, and finally concentrated in Sind Province. The surface runoff induced by rainstorm converged in the junction of Sind and Punjab Province with the pattern of fire hose effect. Subsequently, the flood in Indus River in the Sind Province overflow into the low-lying area along the bank of Indus River due to the terrain of Indus River in these regions has the characteristics of over ground river, and the flood flow capacity is lower than that in northern of Pakistan. In addition, the long-term changes in the flood inundation area can be summarized into four stages: increase slowly period (In June), increase slightly period (In July), increase rapidly period (Between August and the beginning of September), rapidly decline period (After September 15, 2022). Importantly, a conceptual model of disaster caused by the fire pipe effect is summarized based on the comprehensive response of water vapor, rainfall, and topography.

Abstract

Subtropical coastlines are impacted by both tropical and extratropical cyclones. While both may lead to substantial damage to coastal communities, it is difficult to determine the contribution of tropical cyclones to coastal flooding relative to that of extratropical cyclones. We conduct a large-scale flood hazard and impact assessment across the subtropical Southeast Atlantic Coast of the United States, from Virginia to Florida, including different flood hazards. The physics-based hydrodynamic modeling skillfully reproduces coastal water levels based on a comprehensive validation of tides, almost two hundred historical storms, and an in-depth hindcast of Hurricane Florence. We show that yearly flood impacts are two times as likely to be driven by extratropical than tropical cyclones. On the other hand, tropical cyclones are 30 times more likely to affect people during rarer 100-year events than extratropical cyclones and contribute to more than half of the regional flood risk. With increasing sea levels, more areas will be flooded, regardless of whether flooding is driven by tropical or extratropical cyclones. Most of the absolute flood risk is contained in the greater Miami metropolitan area. However, several less populous counties have the highest relative risks. The results of this study provide critical information for understanding the source and frequency of compound flooding across the Southeast Atlantic Coast of the United States.

Abstract

As renewable energy generation increases, the impacts of weather and climate on energy generation and demand become critical to the reliability of the energy system. However, these impacts are often overlooked. Global climate models (GCMs) can be used to understand possible changes to our climate, but their coarse resolution makes them difficult to use in energy system modelling. Here we present open-source generative machine learning methods that produce meteorological data at a nominal spatial resolution of 4 km at an hourly frequency based on inputs from 100 km daily-average GCM data. These methods run 40 times faster than traditional downscaling methods and produce data that have high-resolution spatial and temporal attributes similar to historical datasets. We demonstrate that these methods can be used to downscale projected changes in wind, solar and temperature variables across multiple GCMs including projections for more frequent low-wind and high-temperature events in the Eastern United States.

Abstract

Modeling monthly rainfall erosivity is vital to the optimization of measures to control soil erosion. Rain gauge data combined with satellite observations can aid in enhancing rainfall erosivity estimations. Here, we presented a framework which utilized Geographically Weighted Regression approach to model global monthly rainfall erosivity. The framework integrates long-term (2001–2020) mean annual rainfall erosivity estimates from IMERG (Global Precipitation Measurement (GPM) mission’s Integrated Multi-satellitE Retrievals for GPM) with station data from GloREDa (Global Rainfall Erosivity Database, n = 3,286 stations). The merged mean annual rainfall erosivity was disaggregated into mean monthly values based on monthly rainfall erosivity fractions derived from the original IMERG data. Global mean monthly rainfall erosivity was distinctly seasonal; erosivity peaked at ~ 200 MJ mm ha⁻¹ h⁻¹ month⁻¹ in June–August over the Northern Hemisphere and ~ 700 MJ mm ha⁻¹ h⁻¹ month⁻¹ in December–February over the Southern Hemisphere, contributing to over 60% of the annual rainfall erosivity over large areas in each hemisphere. Rainfall erosivity was ~ 4 times higher during the most erosive months than the least erosive months (December–February and June–August in the Northern and Southern Hemisphere, respectively). The latitudinal distributions of monthly and seasonal rainfall erosivity were highly heterogeneous, with the tropics showing the greatest erosivity. The intra-annual variability of monthly rainfall erosivity was particularly high within 10–30° latitude in both hemispheres. The monthly rainfall erosivity maps can be used for improving spatiotemporal modeling of soil erosion and planning of soil conservation measures.

Abstract

Modeling monthly rainfall erosivity is vital to the optimization of measures to control soil erosion. Rain gauge data combined with satellite observations can aid in enhancing rainfall erosivity estimations. Here, we presented a framework which utilized Geographically Weighted Regression approach to model global monthly rainfall erosivity. The framework integrates long-term (2001–2020) mean annual rainfall erosivity estimates from IMERG (Global Precipitation Measurement (GPM) mission’s Integrated Multi-satellitE Retrievals for GPM) with station data from GloREDa (Global Rainfall Erosivity Database, n = 3,286 stations). The merged mean annual rainfall erosivity was disaggregated into mean monthly values based on monthly rainfall erosivity fractions derived from the original IMERG data. Global mean monthly rainfall erosivity was distinctly seasonal; erosivity peaked at ~ 200 MJ mm ha⁻¹ h⁻¹ month⁻¹ in June–August over the Northern Hemisphere and ~ 700 MJ mm ha⁻¹ h⁻¹ month⁻¹ in December–February over the Southern Hemisphere, contributing to over 60% of the annual rainfall erosivity over large areas in each hemisphere. Rainfall erosivity was ~ 4 times higher during the most erosive months than the least erosive months (December–February and June–August in the Northern and Southern Hemisphere, respectively). The latitudinal distributions of monthly and seasonal rainfall erosivity were highly heterogeneous, with the tropics showing the greatest erosivity. The intra-annual variability of monthly rainfall erosivity was particularly high within 10–30° latitude in both hemispheres. The monthly rainfall erosivity maps can be used for improving spatiotemporal modeling of soil erosion and planning of soil conservation measures.

Abstract

Climate models are vital for understanding and projecting global climate change and its associated impacts. However, these models suffer from biases that limit their accuracy in historical simulations and the trustworthiness of future projections. Addressing these challenges requires addressing internal variability, hindering the direct alignment between model simulations and observations, and thwarting conventional supervised learning methods. Here, we employ an unsupervised Cycle-consistent Generative Adversarial Network (CycleGAN), to correct daily Sea Surface Temperature (SST) simulations from the Community Earth System Model 2 (CESM2). Our results reveal that the CycleGAN not only corrects climatological biases but also improves the simulation of major dynamic modes including the El Niño-Southern Oscillation (ENSO) and the Indian Ocean Dipole mode, as well as SST extremes. Notably, it substantially corrects climatological SST biases, decreasing the globally averaged Root-Mean-Square Error (RMSE) by 58%. Intriguingly, the CycleGAN effectively addresses the well-known excessive westward bias in ENSO SST anomalies, a common issue in climate models that traditional methods, like quantile mapping, struggle to rectify. Additionally, it substantially improves the simulation of SST extremes, raising the pattern correlation coefficient (PCC) from 0.56 to 0.88 and lowering the RMSE from 0.5 to 0.32. This enhancement is attributed to better representations of interannual, intraseasonal, and synoptic scales variabilities. Our study offers a novel approach to correct global SST simulations and underscores its effectiveness across different time scales and primary dynamical modes.