Floods are recognised as one of the most destructive and costliest natural disasters in the world, which impact the lives and livelihoods of millions of people. To tackle the risks associated with flood disasters, there is a need to think beyond structural interventions for flood protection and move to more non-structural ones, such as flood early warning systems (FEWSs). Firstly, this study aimed to uncover how flood forecasting models in the FEWSs have evolved over the past three decades, 1993 to 2023, and to identify challenges and unearth opportunities to assist in model selection for flood prediction. Secondly, the study aimed to assist in model selection and, in return, point to the data and other modelling components required to develop an operational flood early warning system with a focus on data-scarce regions. The scoping literature review (SLR) was carried out through a standardised procedure known as Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). The SLR was conducted using the electronic databases Scopus and Web of Science (WoS) from 1993 until 2023. The results of the SLR found that between 1993 and 2010, time series models (TSMs) were the most dominant models in flood prediction and machine learning (ML) models, mostly artificial neural networks (ANNs), have been the most dominant models from 2011 to present. Additionally, the study found that coupling hydrological, hydraulic, and artificial neural networks (ANN) is the most used ensemble for flooding forecasting in FEWSs due to superior accuracy and ability to bring out uncertainties in the system. The study recognised that there is a challenge of ungauged and poorly gauged rainfall stations in developing countries. This leads to data-scarce situations where ML algorithms like ANNs are required to predict floods. On the other hand, there are opportunities to use Satellite Precipitation Products (SPP) to replace missing or poorly gauged rainfall stations. Finally, the study recommended that interdisciplinary, institutional, and multisectoral collaborations be embraced to bridge this gap so that knowledge is shared for a faster-paced advancement of flood early warning systems.

This review examines South Africa’s escalating flood vulnerability through a synthesis of over 80 peer-reviewed articles, historical records, policy reports, and case studies. Using a PRISMA-guided analysis, the study identifies key climatic drivers, including extreme rainfall from tropical–temperate interactions, cut-off lows, and La Niña conditions that interact with structural weaknesses such as inadequate drainage, poorly maintained stormwater systems, and rapid urban expansion. Apartheid-era spatial planning has further entrenched risk by locating marginalised communities in floodplains. Governance failures like weak disaster risk reduction (DRR) policies, fragmented institutional coordination, and insufficient early warning systems intensify flood vulnerabilities. Catastrophic events in KwaZulu-Natal (KZN) and the Western Cape (WC) illustrate the consequences exemplified by the April 2022 KZN floods alone, which caused over 450 deaths, displaced more than 40,000 people, and generated damages exceeding ZAR 17 billion. Nationally, more than 1500 flood-related fatalities have been documented in the past two decades. Emerging resilience pathways include ecosystem-based adaptation, green infrastructure, participatory governance, integration of Indigenous knowledge, improved hydrological forecasting, and stricter land-use enforcement. These approaches can simultaneously reduce physical risks and address entrenched socio-economic inequalities. However, significant gaps remain in spatial flood modelling, gender-sensitive responses, urban–rural disparities, and policy implementation. The review concludes that South Africa urgently requires integrated, multi-scalar strategies that combine scientific innovation, policy reform, and community-based action. Embedding these insights into disaster management policy and planning is essential to curb escalating losses and build long-term resilience in the face of climate change.

Soil water content or soil moisture content is considered one of the most critical properties of the soil for crop production, irrigation, and environmental studies. The technical development of soil moisture measurement devices is swift, but calibration among field conditions is still not entirely resolved. Accurate calibration requires samples taken right next to the sensor that disturbs the site and changes the soil conditions. Real field operation requires the probe to represent larger areas that have undisturbed soils around the probe. These would describe the parcel’s general soil conditions and start providing data from the time of installation. This study aimed to compare several potential solutions for off-site calibration of an operational EnviroScan sensor (Sentek Technologies, Stepney South, Australia). The performances of the default and soil texture-specific equations provided by the manufacturer were compared with the field and laboratory calibration approaches. Two statistical parameters, coefficient of determination (R²) and root square mean error (RMSE) was used to determine logarithmic model results. The results show that the default calibration equations in all three classes have relatively low performances with RMSE values of around 10–15 and R² values ranging from 0.4 to 0.8. However, significant refinement was achieved by selecting texture-specific equations from the manufacturer’s libraries. The soil texture-specific equations of the EnviroScan often yielded quite satisfactory results, with RMSEs ranging between 2 and 4. Similar RMSE values were achieved from the laboratory calibration exercises, but the reapplication potential of these equations was often questionable due to the severely changed soil conditions of the laboratory processed soil compared to the field soil conditions.

Soil moisture (SM) is a fundamental constituent of the terrestrial environment and the hydrological cycle. Owing to its significant influence on catchment hydrological responses, it can be utilized as an indicator of floods and droughts to aid early warning systems. This study aimed to develop a field‐scale method to estimate SM using parametric and machine learning‐based methods to compare whether advanced artificial intelligence methods can give similar results as traditional methods. Considering this, monthly observed SM data (from the top 10 cm), environmental covariates, and remotely sensed data from March 2019 to July 2021 for the Cathedral Peak Research Catchments VI and IX in South Africa were obtained. From the 241 observations obtained across 12 sites, 160 (∼66%) were used for model training, while the remaining 81 (∼34%) were used for model testing. Employing 10‐fold cross‐validation, the individual machine learning models (viz., support vector machine [SVM], random forest (RF), k‐nearest neighbor, classification and regression trees [Rpart], and generalized linear model) displayed a satisfactory performance ( R ² = 0.52–0.79; root mean square error = 3.79–5.80). In the validation phase, the RF model displayed a superior performance, followed by the SVM. Subsequent SM estimation using the hybrid model produced satisfactory results in training ( R ² = 0.90) and testing ( R ² = 0.45). The results obtained from this study can aid in predicting SM variations in catchments with limited monitoring. Furthermore, this model can be applied in drought monitoring, forecasting, and informing agricultural management practices.

Soil moisture (SM) is a key variable in the climate system and a key parameter in earth surface processes. This study aimed to test the citizen observatory (CO) data to develop a method to estimate surface SM distribution using Sentinel-1B C-band Synthetic Aperture Radar (SAR) and Landsat 8 data; acquired between January 2019 and June 2019. An agricultural region of Tard in western Hungary was chosen as the study area. In situ soil moisture measurements in the uppermost 10 cm were carried out in 36 test fields simultaneously with SAR data acquisition. The effects of environmental covariates and the backscattering coefficient on SM were analyzed to perform SM estimation procedures. Three approaches were developed and compared for a continuous four-month period, using multiple regression analysis, regression-kriging and cokriging with the digital elevation model (DEM), and Sentinel-1B C-band and Landsat 8 images. CO data were evaluated over the landscape by expert knowledge and found to be representative of the major SM distribution processes but also presenting some indifferent short-range variability that was difficult to explain at this scale. The proposed models were evaluated using statistical metrics: The coefficient of determination (R²) and root mean square error (RMSE). Multiple linear regression provides more realistic spatial patterns over the landscape, even in a data-poor environment. Regression kriging was found to be a potential tool to refine the results, while ordinary cokriging was found to be less effective. The obtained results showed that CO data complemented with Sentinel-1B SAR, Landsat 8, and terrain data has the potential to estimate and map soil moisture content.

Flooding has increasingly posed significant challenges in the Western Cape, South Africa, with the September 2023 floods in Franschhoek underscoring the vulnerability of the region to extreme rainfall events. During this event, the area received over 220 mm of rainfall within 48 hours, resulting in extensive flooding that inundated approximately 500 hectares, displaced over 1,000 residents, and caused substantial damage to infrastructure. This study developed an integrated Flood Risk Information System (FRIS) designed for flood-prone regions in the Western Cape, utilizing Earth Observation (EO) technologies, hydrological modelling, and Geographic Information Systems (GIS).The system integrated historical flood data, municipal hydrological observations, and real-time environmental variables, including rainfall, river discharge, and soil moisture, to enhance flood risk prediction, monitoring, and response. Hydrological modelling was conducted using the HEC-HMS (Hydrologic Engineering Center’s Hydrologic Modeling System) and SWAT (Soil and Water Assessment Tool) models. Machine learning algorithms, including Random Forest (RF) and Gradient Boosting Machine (GBM), were implemented to predict flood probabilities. Model outputs were validated against observed data from the local municipality, which included flood extent maps and river discharge measurements.The system demonstrated high accuracy in predicting flood extents, with the HEC-HMS model achieving a Nash-Sutcliffe Efficiency (NSE) of 0.88 and a Root Mean Square Error (RMSE) of 12% compared to observed discharge data. The machine learning models yielded flood prediction accuracies of 87% (RF) and 91% (GBM) when compared to observed flood extents. Google Earth Engine (GEE) was used to process large EO datasets, allowing for real-time flood mapping and risk analysis.The FRIS proved instrumental in being able to model the September 2023 floods by providing accurate predictions and mapping, enabling disaster management agencies to target evacuation efforts and allocate resources effectively. However, further improvements are planned, including incorporating finer-resolution rainfall and topographic data, expanding the system’s spatial coverage, and integrating socio-economic indicators to assess community vulnerability better.This study highlighted the potential of combining EO, GEE, GIS, and advanced hydrological models in improving flood risk management. The FRIS provides a powerful framework for mitigating flood impacts and protecting vulnerable communities, aligning with broader efforts to enhance climate adaptation and resilience in the Western Cape.