In Lake Ziway watershed in Ethiopia, the contribution of river inflow to the water level has not been quantified due to scarce data for rainfall-runoff modeling. However, satellite rainfall estimates may serve as an alternative data source for model...
This study evaluates the impact of sub-daily bias correction of gridded rainfall products (RPs) on the estimation rainfall erosivity in Burkina Faso (West African Sahel). Selected RPs, offering half-hourly to hourly rainfall, are assessed against 10...
With the advances in remote sensing technology, satellite-based rainfall estimates are gaining attraction in the field of hydrology, particularly in rainfall-runoff modeling. Since estimates are affected by errors correction is required. In this stud...
Advances in remote sensing have led to the use of satellite-derived rainfall products to complement the sparse rain gauge data. Although these products are globally and some regional bias corrected, they often show substantial differences relative to...
Accurate rainfall information is essential for rainfall–runoff modeling in monsoon-influenced basins, where pronounced spatial variability and limited gauge coverage introduce significant uncertainty. Satellite precipitation products provide sp...
This study proposes a new method to estimate the bias correction ratio for the rainfall forecast to be used as input for a flash flood warning system. This method requires a backward tracking to locate where the forecasted storm is at the present tim...
Results of numerous evaluation studies indicated that satellite-rainfall products are contaminated with significant systematic and random errors. Therefore, such products may require refinement and correction before being used for hydrologic applicat...
Many satellite soil moisture products are today globally available in near real-time. These observations are of paramount importance for enhancing the understanding of the hydrological cycle and particularly useful for flood forecasting purposes. In...
This study compares the bias correction techniques of empirical quantile mapping (QM) and the Long Short-Term Memory (LSTM) machine learning model for summertime daily rainfall simulation focusing on precipitation-dependent bias and temporal variatio...
This study investigates the relationship between key circulation factors and ensemble forecast uncertainty during an atypical extreme heavy-rainfall event under weak synoptic forcing that affected the Guangzhou–Foshan megacity cluster in the Pe...
The Meghna River basin is a transboundary basin that lies in Bangladesh (~40%) and India (~60%). Due to its terrain structure, the Bangladesh portion of the basin experiences frequent floods that cause severe human and economic losses. Bangladesh, as...
To test how forecast-rainfall errors should be handled in small-stream water-level forecasting, we evaluated the two-stage disaggregation in Korean Patent 10-2619596: uniform 10 min splitting of hourly forecasts (stage 1) and splitting only the remai...
High-quality rainfall data are crucial for accurately forecasting flash floods and runoff simulations. However, traditional correction methods often overlook errors in rainfall-monitoring data. We established a screening system to identify anomalous...
This study compares multi model ensemble (MME) projections of rainfall using general quantile mapping, gamma quantile mapping, Power Transformation and Linear Scaling bias correction (BC) methods for representative concentration pathways (RCPs) 4.5 a...
Rainfall intensity plays an important role in landslide prediction especially in mountain areas. However, the rainfall intensity of a location is usually interpolated from rainfall recorded at nearby gauges without considering any possible effects of...
Satellites offer a way of estimating rainfall away from rain gauges which can be utilised to overcome the limitations imposed by gauge density on traditional rain gauge analyses. In this study, Australian station data along with the Japan Aerospace E...
This paper proposes a new post-processing method for model data in order to improve typhoon track and rainfall forecasts. The model data used in the article include low-resolution ensemble forecasts and high-resolution forecasts. The entire improveme...
Accurate rainfall data with good spatial–temporal distribution remain a challenge worldwide, particularly in arid regions such as western Saudi Arabia, where variability critically influences water resource management and flood mitigation. This...
The Philippines experiences intense rainfall but has limited ground-based monitoring infrastructure for flood prediction. Satellite rainfall products provide broad coverage but contain systematic biases that reduce operational usefulness. This study...
X-band rainfall radar systems offer advantages such as high spatial resolution, flexible deployment options, and strong near-surface detection capabilities. However, due to the short electromagnetic wavelength in this band, the radar base data are hi...
Precise estimation of the spatial and temporal characteristics of rainfall is essential for producing the reliable catchment response needed for proper management of water resources. However, in most parts of the world, gauged rainfall stations are s...
Morocco’s meteorological observation network is quite old, but the spatial coverage is insufficient to conduct studies over large areas, especially in mountainous regions, such as the Fez-Meknes region, where spatio-temporal variability in prec...
This study aimed to enhance the accuracy of extreme rainfall forecast, using a machine learning technique for forecasting hydrological impact. In this study, machine learning with XGBoost technique was applied for correcting the quantitative precipit...
Reliable rainfall data are critical for managing hydrometeorological hazards in West Africa, yet they are often sparse and temporally inconsistent. The current study assessed the accuracy of four near real-time satellite-based rainfall data, namely I...
Climate change affects the intensity of extreme rainfall, which is an important factor for design flood assessment and dam safety, particularly in the monsoon region of Southeast Asia and the Mekong Basin, where seasonal rainfall variability is high...
The availability of accurate and reliable rainfall data that are applicable to various phenomenological, climatological, and modeling studies is important, especially in the Philippines, which is considered to be highly vulnerable to natural hazards...
As the limitation of rainfall collection by ground measurement has been widely recognized, satellite-based rainfall estimate is a promising high-resolution alternative in both time and space. This study is aimed at exploring the capacity of the satel...
The reliability of weather radar data in real-time flood forecasting and early warning system remain ambivalent due to high uncertainty in Quantitative Precipitation Forecasts (QPF). In this study, a methodology is presented with the objective to imp...
The Dual-frequency Precipitation Radar (DPR) aboard the Global Precipitation Measurement (GPM) mission provides valuable three-dimensional precipitation structure data on a global scale and has been widely used in hydrometeorological research. Howeve...
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...
This study of the Quantitative Estimation Precipitation (QEP) of rainfall, detected by two Meteorology Radars over Chi Basin, North-east Thailand, used data from the Thai Meteorological Department (TMD). The rainfall data from 129 rain gauge stations...
Climate change, marked by decreasing rainfall and increasing extreme events, represents a major challenge for water resources, particularly in semi-arid regions. To estimate aquifer recharge, it is essential to assess the fraction of precipitation co...
X-band weather radar can provide high spatial and temporal resolution data, which is essential to precipitation observation and prediction of mesoscale and microscale weather. However, X-band weather radar is susceptible to precipitation attenuation....
The single polarization C-Band weather radar network of the Meteorological Service of Catalonia covers the entire region (32,000 km2), which allows it to apply a series of corrections that improve preliminary estimations of the rainfall field (hourly...
Reconstructing long-term continuous precipitation series is essential for characterising rainfall variability, detecting hydroclimatic trends, and supporting water resource management in semi-arid Mediterranean regions. This study analyses rainfall v...
This study investigates the scaling properties of rainfall in Tunis over temporal scales ranging from 5 min to 2.5 years using high-resolution rain gauge data from three recording stations. We employ the Universal Multifractal (UM) framework to chara...
This work aims to evaluate the performance of a hydrological balance model in a watershed located in northern Tunisia (wadi Sejnane, 378 km2) in present climate conditions using input variables provided by four regional climate models. A modified ver...
This study investigates the utility of satellite-based rainfall estimates in simulating flash floods in Karpuz River Basin, Turkey, characterized by limited rain gauge network. Global Satellite Mapping of Precipitation (GSMaP) product was evaluated w...
Senegal, like many West African countries reliant on natural resources and agriculture, faces severe impacts from climate change. This study provides an analysis undertaken by the United States Agency for International Development (USAID) under the S...
This research addresses the challenge of bias in Remotely Sensed Rainfall (RSR) datasets used for hydrological planning in Uganda’s data-scarce, ungauged catchments. Four bias correction methods, Quantile Mapping (QM), Linear Transformation (LT...
Satellite observations (Copernicus Sentinell-1) can supply antecedent soil moisture data, which helps to predict thresholds triggering runoff and runoff volume. In the paper, we developed a runoff correction factor to the USLE, using rainfall and sat...
Accurate rainfall estimates are essential for managing water resources and planning for climate risks in semi-arid regions, yet long-term gauge networks in these environments are often extremely limited. In this study, we evaluate three widely used m...
Scientists who want to know future climate can use multimodel ensemble (MME) methods that combine projections from individual simulation models. To predict the future changes of extreme rainfall in Iran, we examined the observations and 24 models of...
The dynamic system response curve (DSRC) is commonly applied as a real-time flood forecasting error correction method to improve the accuracy of real-time flood forecasting. It has been widely recognized that the least squares (OLS/LS) method, employ...
Ground-based weather radars are essential to better understand precipitation systems, to improve the Quantitative Precipitation Estimation (QPE), and to subsequently provide input to hydrological models. However, reflectivity measured by radars is ty...
Microwave radiometers are vital for global ocean observations, yet they are prone to errors from radio frequency interference, sun glint, and other contamination. This paper focuses on the newly launched Chinese FY-3G satellite’s Microwave Radi...
Soil erosion poses a significant environmental challenge in semi-arid and Mediterranean regions, jeopardizing the sustainability of land and water resources. This study explores the spatio-temporal dynamics of water erosion within the Tafna watershed...
In the original publication [...]
The Tropical Rainfall Measuring Mission (TRMM) and Global Precipitation Mission (GPM) are the most important and widely used data sources in several applications—e.g., forecasting drought and flood, and managing water resources—especially...
A method that formulates the retrieval of drop size distribution (DSD) parameters from polarimetric radar variables at attenuating frequency as the solution of an inverse problem is presented. The DSD in each radar bin is represented by a normalized...
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