Search Results (9)

Floods result from intense and/or prolonged rainfall that exceeds the soil’s infiltration capacity, generating surface runoff and increasing river discharge. These events can cause substantial societal damage and may even lead to fatalities. In this study, we analyzed flood events in Lençóis Paulista, southeastern Brazil, between 2016 and 2024, by evaluating estimated precipitation and soil moisture conditions to develop a flood prediction index for the city. Precipitation estimates were derived from reflectivity data provided by the Bauru weather radar, while soil moisture estimates were obtained from the Joint UK Land Environment Simulator (JULES) land surface model, operated at IPMet-Unesp. Although the index was not developed based on formal hydrological modeling or physical process simulation, the analysis of these variables within the Lençóis River sub-basins revealed that elevated soil moisture in the days preceding flood events was a key contributing factor. This is consistent with the increased susceptibility of wetter soils to surface runoff generation. Based on the identification of relevant variables, we developed the Flood Probability Index (FPI) using data from only nine flood events and applied it to classify the likelihood of flooding in the city. The index produced satisfactory results, highlighting its potential as a tool for flood prediction and early warning for the local population. Full article

Floods are a significant and pervasive threat globally, exacerbated by climate change and increasing extreme weather events. The Gravity Recovery and Climate Experiment (GRACE) and its follow-on mission (GRACE-FO) provide crucial insights into terrestrial water storage anomalies (TWSA), which are vital for understanding flood dynamics. However, the observational gap between these missions presents challenges for flood monitoring, affecting the estimation of long-term trends and limiting the analysis of interannual variability, thereby impacting overall analysis accuracy. Reconstructing the missing data between GRACE and GRACE-FO is essential for systematically understanding the spatiotemporal distribution characteristics and driving mechanisms of interannual changes in regional water reserves. In this study, the Generative Adversarial Imputation Network (GAIN) is applied to improve the monitoring capability for flood events in the Pearl River Basin (PRB). First, the GRACE/GRACE-FO TWSA data gap is imputed with GAIN and compared with long short-term memory (LSTM) and k-Nearest Neighbors (KNN) methods. Using the reconstructed data, we develop the Flood Potential Index (FPI) by integrating GRACE-based TWSA with precipitation data and analyze key characteristics of FPI variability against actual flood events. The results indicate that GAIN effectively predicts the GRACE/GRACE-FO TWSA gap, with an average improvement of approximately 50.94% over LSTM and 68.27% over KNN. The reconstructed FPI proves effective in monitoring flood events in the PRB, validating the reliability of the reconstructed TWSA. Additionally, the FPI achieves a predictive accuracy of 79.7% for real flood events, indicating that short-term flood characteristics are better captured using TWSA. This study demonstrates the effectiveness of GAIN in enhancing data continuity, providing a reliable framework for large-scale flood risk assessment and offering valuable insights for flood management in vulnerable regions. Full article

Areas subject to flooding must be carefully analyzed to make correct measures for preventing disasters that impact the population’s lives and economy. In this article, we propose a flood potential index (FPI) to estimate flood susceptibility, using an optimal selection of weights for the criteria contributing to flooding risk evaluation. Comparisons with the situation when equal weights are assigned to each factor are exemplified in a case study from the Vărbilău catchment (Romania). The study reveals the necessity of an objective factor weighting choice for determining the flooded zones. The results are validated with the available data from the Romanian Waters Institute. Full article

Floods have always threatened the survival and development of human beings. To reduce the adverse effects of floods, it is very important to understand the influencing factors of floods and their formation mechanisms. In our study, we integrated the Gravity Recovery and Climate Experiment and its Follow-On and Swarm solutions to estimate an uninterrupted 19-year flood potential index (FPI) time series, discussed the spatiotemporal distribution characteristics of the FPI and monitored major floods in the Volga River basin (VRB) from 2003 to 2021. Finally, we analyzed the relationship between the FPI and hydrometeorological factors to comprehend the flood formation mechanism. The results show that data fusion has reduced the uncertainty of terrestrial water storage change (TWSC), and the TWSC from the combined satellite gravity observations has a good consistency with that from the Global Land Data Assimilation System model (correlation coefficient = 0.92). During the study period, two major floods (June 2005 and May 2018) occurred in the VRB. The FPI has a significant seasonal change characteristic, and shows a high flood risk in spring and a low one in autumn. With regards to spatial distribution, the flood risk is increasing in the north (increasing rate = 0.1) and decreasing in the south (decreasing rate = 0.39). Snow water equivalent (SWE, correlation coefficient = 0.75) has a stronger correlation with the FPI than precipitation (PPT, correlation coefficient = 0.46), which is attributed to the recharge of SWE on water resources greater than that of PPT. The rising surface temperature (ST) speeds up snow melt, resulting in excessive groundwater and soil moisture, and the flood risk greatly increases at this time. The process lasts about three months. Therefore, except for PPT, ST is also a climatic factor leading to the floods in the VRB. Our study provides a reference for flood research in high-latitude regions. Full article

Flood events have caused huge disasters with regard to human life and economic development, especially short-term flood events that have occurred in recent years. Gravity Recovery and Climate Experiment (GRACE) satellites can directly detect the spatiotemporal characteristics of terrestrial water storage anomalies (TWSA), which play an important role in capturing flood signals. However, the monthly resolution of GRACE-derived TWSA limits its application in monitoring sub-monthly flood events. Therefore, this paper first reconstructs the daily TWSA based on a statistical model with near real-time precipitation and temperature as input variables, and then three daily flood monitoring indexes are developed based on the reconstructed TWSA. Furthermore, these indexes are employed to evaluate the temporal and spatial characteristics of the 2016 short-term flood event in the Haihe River basin (HRB), including the flood potential index (FPI), water storage deficit index (WSDI), and combined climate deviation index (CCDI). In contrast to previous studies, the temporal resolution of TWSA-based indexes is improved from the monthly scale to the daily scale, which largely improves the temporal characterization of flood monitoring. Results demonstrate that (1) among ten kinds of “Temperature-Precipitation” combinations, the reconstructed TWSA based on CN05.1-CN05.1 match well with the GRACE TWSA, as well as publicly available daily TWSA datasets with a Nash-Sutcliffe efficiency coefficient (NSE) of 0.96 and 0.52 ~ 0.81 respectively. (2) The short-term flood characteristics can be better characterized by the reconstructed daily TWSA based on CN05.1-CN05.1, reaching the peak of 216.19 mm on July 20 in the flood center. Additionally, the spatial characteristics of the equivalent water height (EWH) are detected to evolve from southwest to northeast during the short-term flood. (3) FPI, WSDI, and CCDI are proven to be effective in monitoring flood events in the HRB, which validates the reliability of the reconstructed daily TWSA. Moreover, compared to the 56% and 66% coverage of damage quantified by FPI and CCDI, the 45% damage coverage of the flood mapped by WSDI is more consistent with the governmental reports within the HRB. This paper is expected to provide a valuable reference for the assessment of short-term events caused by extreme climate change. Full article

In a changing environment, changes in terrestrial water storage (TWS) in basins have a significant impact on potential floods and affect flood risk assessment. Therefore, we aimed to study the impact of TWS on potential floods. In this study, we reconstructed the TWS based on precipitation and temperature, evaluated the reconstructed TWS data based on Gravity Recovery and Climate Experiment (GRACE)-TWS data, and analyzed and calculated the flood potential index (FPI) in the Yangtze River Basin (YRB). The related influencing factors were analyzed based on the Global Land Data Assimilation System (GLDAS) data and Granger’s causality test. The main conclusions are as follows: (1) although the GRACE-TWS anomaly (GRACE-TWSA) in the YRB showed an increasing trend for the averaged TWSA over all grids in the whole basin (i.e., 0.31 cm/a, p < 0.05), the variable infiltration capacity-soil moisture anomalies (VIC-SMA) showed a decreasing trend (i.e., −0.048 cm/a, p > 0.05) during April 2002–December 2019; (2) a larger relative contribution of detrended precipitation to FPI was found in the Jialingjiang River Basin (JRB), Wujiang River Basin (WRB), Dongting Lake Rivers Basin (DLRB), YinBin-Yichang reaches (YB-YC), and Yichang-Hukou reaches (YC-HK), while the contribution of detrended TWS to FPI in the Poyang Lake Rivers Basin (PLRB) was larger than that in other basins; and (3) the original and detrended soil moisture (SM) and TWS in the YRB showed a significant positive correlation (p < 0.05), while the significant effect of SM on TWS caused a change in FPI in the YRB and its sub-basins. This study is of great significance for the correct understanding of the FPI and the accurate assessment of flood risk. Full article

The importance of identifying the areas vulnerable for both floods and flash-floods is an important component of risk management. The assessment of vulnerable areas is a major challenge in the scientific world. The aim of this study is to provide a methodology-oriented study of how to identify the areas vulnerable to floods and flash-floods in the Buzău river catchment by computing two indices: the Flash-Flood Potential Index (FFPI) for the mountainous and the Sub-Carpathian areas, and the Flood Potential Index (FPI) for the low-altitude areas, using the frequency ratio (FR), a bivariate statistical model, the Multilayer Perceptron Neural Networks (MLP), and the ensemble model MLP–FR. A database containing historical flood locations (168 flood locations) and the areas with torrentiality (172 locations with torrentiality) was created and used to train and test the models. The resulting models were computed using GIS techniques, thus resulting the flood and flash-flood vulnerability maps. The results show that the MLP–FR hybrid model had the most performance. The use of the two indices represents a preliminary step in creating flood vulnerability maps, which could represent an important tool for local authorities and a support for flood risk management policies. Full article

Floods frequently occur in Nigeria. The catastrophic 2012 flood in Nigeria claimed 363 lives and affected about seven million people. A total loss of about 2.29 trillion Naira (7.2 billion US Dollars) was estimated. The effect of flooding in the country has been devastating because of sparse to no flood monitoring, and a lack of an effective early flood warning system in the country. Here, we evaluated the efficacy of using the Gravity Recovery and Climate Experiment (GRACE) terrestrial water storage anomaly (TWSA) to evaluate the hydrological conditions of the Lower Niger River Basin (LNRB) in Nigeria in terms of precipitation and antecedent terrestrial water storage prior to the 2012 flood event. Furthermore, we accessed the potential of the GRACE-based flood potential index (FPI) at correctly predicting previous floods, especially the devastating 2012 flood event. For validation, we compared the GRACE terrestrial water storage capacity (TWSC) quantitatively and qualitatively to the water budget of TWSC and Dartmouth Flood Observatory (DFO) respectively. Furthermore, we derived a water budget-based FPI using Reager’s methodology and compared it to the GRACE-derived FPI quantitatively. Generally, the GRACE TWSC estimates showed seasonal consistency with the water budget TWSC estimates with a correlation coefficient of 0.8. The comparison between the GRACE-derived FPI and water budget-derived FPI gave a correlation coefficient of 0.9 and also agreed well with the flood reported by the DFO. Also, the FPI showed a marked increase with precipitation which implies that rainfall is the main cause of flooding in the study area. Additionally, the computed GRACE-based storage deficit revealed that there was a decrease in water storage prior to the flooding month while the FPI increased. Hence, the GRACE-based FPI and storage deficit when supplemented with water budget-based FPI could suggest a potential for flood prediction and water storage monitoring respectively. Full article

Floods have caused tremendous economic, societal and ecological losses in the Yangtze River Basin (YRB) of China. To reduce the impact of these disasters, it is important to understand the variables affecting the hydrological state of the basin. In this study, we used Gravity Recovery and Climate Experiment (GRACE) satellite data, flood potential index (FPI), precipitation data (Tropical Rainfall Measuring Mission, TRMM 3B43), and other meteorological data to generate monthly terrestrial water storage anomalies (TWSA) and to evaluate flood potential in the YRB. The results indicate that the basin contained increasing amounts of water from 2003 to 2014, with a slight increase of 3.04 mm/year in the TWSA. The TWSA and TRMM data exhibit marked seasonal characteristics with summer peaks and winter dips. Estimates of terrestrial water storage based on GRACE, measured as FPI, are critical for understanding and predicting flooding. The 2010 flood (FPI ~ 0.36) was identified as the most serious disaster during the study period, with discharge and precipitation values 37.95% and 19.44% higher, respectively, than multi-year average values for the same period. FPI can assess reliably hydrological extremes with high spatial and temporal resolution, but currently, it is not suitable for smaller and/or short-term flood events. Thus, we conclude that GRACE data can be effectively used for monitoring and examining large floods in the YRB and elsewhere, thus improving the current knowledge and presenting potentially important political and economic implications. Full article