Accurate forecasting of wind speeds is a crucial aspect of providing fine-scale professional meteorological services (such as wind energy generation and transportation operations etc.). This article utilizes CMA-MESO model forecast data and CARAS-SUR...
Modeling the hydrologic responses to future changes of climate is important for improving adaptive water management. In the present application to the Zarrine River Basin (ZRB), with the major reach being the main inflow source of Lake Urmia (LU), fi...
Extreme weather events caused by climate change affect the growth of crops, requiring reliable weather forecasts. In order to provide day-to-season seamless forecasting data for the agricultural sector, improving the forecasting performance of the S2...
High-resolution atmospheric modeling over topographically complex coastal zones requires multi-source spatial data fusion to bridge the scale gap between satellite-assimilated global reanalysis and localized boundary layer dynamics. This study presen...
Accurate high-spatial-resolution precipitation is significantly important in hydrological and meteorological modelling, especially in rain-gauge-sparse areas. Some methods and strategies have been applied for satellite-based precipitation downscaling...
Water planning in semi-arid regions depends on climate information that resolves both seasonal timing and topographic gradients. This study evaluated 15 CMIP6 models over Zacatecas, Mexico, and produced a 1 km historical dataset for 1985–2014 b...
Fine spatial information of precipitation plays a significant role in regional eco-hydrological studies but remain challenging to derive from satellite observations, especially in complex terrain areas. Sichuan Province, located in the southwest of C...
Bangladesh, one of the most vulnerable countries to climate change, has been experiencing significant climate change-induced risks. Particularly, the northwest region of the country has been severely affected by climate extremes, including droughts a...
With improvements in data quality and technology, the statistical downscaling data of General Circulation Models (GCMs) for climate change impact assessment have been refined from monthly data to daily data, which has greatly promoted the data applic...
The evaluation of offshore wind energy resources is important to the construction of offshore wind power facilities. In this paper, using four models from CMIP6 and the ERA5 reanalysis dataset, a deep learning model termed SwinWind was developed and...
The Qinghai-Tibetan Plateau (QTP), the source of many major Asian rivers, is sensitive to climate change, affecting billions of people’s livelihoods across Asia. Here, we developed high-resolution projections of precipitation and daily maximum/...
This study assesses the use of Quantile-Mapping methods for bias correction and downscaling in climate change studies. A “Perfect Model Experiment” is conducted using high-resolution climate simulations as pseudo-references and coarser ve...
Intensive groundwater exploitation in the Wei River Basin (WRB) has caused severe depletion. While Gravity Recovery and Climate Experiment (GRACE) satellites monitor these changes, their coarse resolution fails to account for soil erosion-induced mas...
The impacts of climate change on the water availability of Zarrine River Basin (ZRB), the headwater of Lake Urmia, in western Iran, with the Boukan Dam, are simulated under various climate scenarios up to year 2029, using the SWAT hydrological model....
The climate is changing and its impacts on agriculture are a major concern worldwide. The impact of precipitation will influence crop yield and water management. Estimation of such impacts using inputs from the General Circulation Models (GCMs) for f...
With an ever-growing recognition of Land Surface Temperature (LST) as a key Essential Climate Variable (ECV), it becomes utmost important to have such a variable at the fine spatial and temporal scales of urban spaces and dynamics. Sentinel-3 provide...
The accurate estimation of forest above-ground biomass (AGB) is vital for monitoring changes in forest carbon sinks. However, the spatial heterogeneity of AGB, coupled with inherent uncertainties, poses challenges in acquiring high-quality AGBs. This...
A prequel study showed that dynamic downscaling using a regional climate model (RCM) over Africa improved the Goddard Institute for Space Studies Atmosphere-Ocean Global Climate Model (GISS AOGCM: ModelE) simulation of June–September rainfall p...
The authors wish to make the following corrections to this paper [...]
The rescaling of rainfall requires measurements of rainfall rates over many dimensions. This paper develops one approach using 10 m vertical spatial observations of the Doppler spectra of falling rain every 10 s over intervals varying from 15 up to 4...
The paper compares future streamflow projections for 133 catchments in the Murray–Darling Basin simulated by a hydrological model with future rainfall inputs generated from different methods informed by climate change signals from different glo...
In future years, extreme weather events are expected to frequently increase due to climate change, especially in the combination of climate change and events of El Niño–Southern Oscillation. This pays special attention to the constructio...
The demand for high-precision 3D wind fields in low-altitude weather services is increasing. To improve reconstruction accuracy, the high precision of 2D near-surface single-level products and the vertical structure of 3D numerical model outputs are...
The systemic biases of Regional Climate Models (RCMs) impede their application in regional hydrological climate-change effects analysis and lead to errors. As a consequence, bias correction has become a necessary prerequisite for the study of climate...
Remote sensing techniques provide data on the spatial–temporal distribution of environmental parameters over regions with sparse ground observations. However, the resolution of satellite precipitation data is too coarse to be applied to hydrolo...
Reliable climate data are essential for hydroclimatic assessment and water-resources management in data-scarce regions. This study evaluated the performance of the WorldClim 2.1 historical weather dataset (WC2.1– CRU-TS v4.09), downscaled and b...
In the original publication [...]
This paper compared the predictive performance of different regression models for trend component estimation in the spatial downscaling of coarse resolution satellite data using area-to-point regression kriging in the context of the sensitivity to in...
This study presents a process-informed framework for assessing the differential credibility of diverse downscaling methodologies, including both statistical (simple and complex) and dynamical approaches. The methods evaluated include a convolutional...
29 February 2016
Foodborne diseases have large economic and societal impacts worldwide. To evaluate how the risks of foodborne diseases might change in response to climate change, credible and usable climate information tailored to the specific application question i...
Satellites are capable of observing precipitation over large areas and are particularly suitable for estimating precipitation in high mountains and poorly gauged regions. However, the coarse resolution and relatively low accuracy of satellites limit...
Remote sensing soil moisture (SM) has been widely used in various earth science studies and applications, but their low resolution limits their usage and downscaling of them is needed. In this study, we proposed a spatial downscaling method for SM ba...
Precipitation plays an important role in the global water cycle, in addition to material and energy exchange processes. Therefore, obtaining precipitation data with a high spatial resolution is of great significance. We used a geographically weighted...
Climate parameter projections obtained by global and regional models (GCM and RCM, respectively) offer a challenge to many researchers in terms of controlling the quality of the outcome data using several scales. In the literature, the proposed model...
Passive microwave surface soil moisture (SSM) products tend to have very low resolution, which massively limits their application and validation in regional or local-scale areas. Many climate and hydrological studies are urgently needed to evaluate t...
Budapest, the capital of Hungary, has been facing serious air pollution episodes in the heating season similar to other metropolises. In the city a dense urban air quality monitoring network is available; however, air quality prediction is still chal...
Satellite precipitation products alleviate sparse gauge coverage, but their coarse resolution limits hydrometeorological applications. This study develops and evaluates an integrated downscaling–calibration framework for the Yellow River Basin,...
In mountainous regions, the scarcity of air temperature (Ta) measurements is a major limitation for hydrological and crop monitoring. An alternative to in situ measurements could be to downscale the reanalysis Ta data provided at high-temporal resolu...
The downscaling of frequently-acquired geostationary Land Surface Temperature (LST) data can compensate the lack of high spatiotemporal LST data for urban climate studies. In order to be usable, the generated datasets must accurately reproduce the sp...
Accurate precipitation data at a high spatial resolution are essential for hydrological, meteorological, and ecological research at regional scales. This study presented a geostatistical downscaling-calibration procedure to derive the high spatial re...
Environmental monitoring of Earth from space has provided invaluable information for understanding land–atmosphere water and energy exchanges. However, the use of satellite-based precipitation observations in hydrologic and environmental applications...
In this study, half-hourly Global Precipitation Mission (GPM) satellite precipitation data were downscaled to produce high-resolution daily rainfall data for tropical coastal micro-watersheds (100–1000 ha) without gauges or with rainfall data c...
A downscaling framework for coarse resolution Gravity Recovery and Climate Experiment (GRACE) Total Water Storage Anomaly (TWSA) data is described, exploiting the observations of precipitation from the Global Precipitation Measurement (GPM) mission,...
Both the global circulation model (GCM) and regional climate model (RCM) simulations suffer from model biases that eventually result in significant errors in regional forecasts. This model bias issue is addressed using the bias correction approach. T...
Hydrologic models driven by downscaled meteorologic data from general circulation models (GCM) should be evaluated using long-term simulations over a historical period. However, simulations driven by GCM data cannot be directly evaluated using observ...
Taihu Lake is a large lake with high levels of eutrophication. Cyanobacterial outbreaks significantly affect the ecological environment and socioeconomic development. The chlorophyll-a (Chl-a) concentration, which is crucial for monitoring eutrophica...
Precipitation as a key parameter in hydrometeorology and other water-related applications always needs precise methods for assessing and predicting precipitation data. In this study, an effort has been conducted to downscale and evaluate a satellite...
Low accuracy and coarse spatial resolution are the two main drawbacks of satellite precipitation products. Therefore, calibration and downscaling are necessary before these products are applied. This study proposes a two-step framework to improve the...
Designed for rainstorms and flooding, hydrosystems are largely based on local rainfall Intensity–Duration–Frequency (IDF) curves which include nonstationary components accounting for climate variability. IDF curves are commonly calculated using downs...
Addressing the challenges posed by sparse ground meteorological stations and the insufficient resolution and accuracy of reanalysis and satellite precipitation products, this study establishes a multi-source environmental feature system that precisel...
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