Climate change has led to an increase in global temperature and frequent intense precipitation, resulting in a rise in severe and intense urban flooding worldwide. This growing threat is exacerbated by rapid urbanization, impervious surface expansion, and overwhelmed drainage systems, particularly in urban regions. As urban flooding becomes more catastrophic and causes significant environmental and property damage, there is an urgent need to understand and address urban flood susceptibility to mitigate future damage. This review aims to evaluate remote sensing datasets and key parameters influencing urban flood susceptibility and provide a comprehensive overview of the flood causative factors utilized in urban flood susceptibility mapping. This review also highlights the evolution of traditional, data-driven, big data, GISs (geographic information systems), and machine learning approaches and discusses the advantages and limitations of different urban flood mapping approaches. By evaluating the challenges associated with current flood mapping practices, this paper offers insights into future directions for improving urban flood management strategies. Understanding urban flood mapping approaches and identifying a foundation for developing more effective and resilient urban flood management practices will be beneficial for mitigating future urban flood damage.

It is crucial to understand the development of hydrological drought which is unique to a sub-basin to derive management strategies that can address the cause. In this study, relationships between climate and catchment control against hydrological drought development in the Tekeze River Basin (TRB), Ethiopia, were assessed. The Water Evaluation and Planning (WEAP) modeling tool was selected to mimic the behavior and historical characteristics of the basin which was modeled for the period 1981 to 2018. The most severe drought events and historical drought years were selected and analyzed on a monthly basis, where the classical rainfall deficit drought was identified to be the most common typology within the basin. Once modeled, both meteorological and hydrological drought analyses were performed using the Threshold Level Method (TLM) where 168 months of meteorological drought with magnitudes as high as 110 mm/month and 60 months of streamflow anomalies with magnitudes of up to 17 mm/month were observed. While the temporal resolution impacts results pertaining to hydrological drought development, the analysis showed that the basin is fast responding, where storage characteristics did not play a significant role in delaying a hydrological drought onset. Compared to naturalized streamflow, the construction of the Tekeze Dam on the main river was indicative of an over 900% increase in dry season flows but a reduction of 23% of wet season flows, showing the potential to redistribute runoff in space and time.

In semiarid, fragmented landscapes where data scarcity challenges effective land management, accurate soil moisture monitoring is critical. This study presents a high-resolution analysis that integrates remote sensing, in situ data, and machine learning to predict soil moisture and evaluate the impact of land conservation practices. A Long Short-Term Memory (LSTM) model combined with Random Forest gap-filling achieved strong predictive performance (R² = 0.84; RMSE = 0.103 cm³ cm⁻³), outperforming SMAP satellite estimates by approximately 30% across key accuracy metrics. The model was applied to 222 field sites in northern Ghana to quantify the effects of stone bunds on soil moisture retention. The results revealed that fields with stone bunds maintained 4–6% higher moisture than non-bunded fields, particularly on steep slopes and in areas with low to moderate topographic wetness. These findings demonstrate the capability of combining remote sensing and deep learning for fine-scale soil-moisture prediction and provide quantitative evidence of how nature-based solutions enhance water retention and climate resilience in dryland agricultural systems.

Understanding agricultural water use by sources such as rainwater (green water) or irrigation water (blue water) is vital to manage scarce freshwater resources. Tracing sources of evapotranspiration increases water management efficiency. This study proposes a two-bucket soil moisture water balance approach to trace sources of evapotranspiration over irrigated landscapes in the Awash Basin, Ethiopia, which is modeled in the water evaluation and planning model. Algorithms are devised to separate actual evapotranspiration into blue and green sources. The results showed annual basin renewable water of 15.78 BCM in the form of streamflow (4.65 BCM), stored water (3.54 BCM), and groundwater percolation (7.59 BCM). Annual actual evapotranspiration over irrigated landscapes was 805 MCM, which was separated as blue evapotranspiration (88.4%) and green evapotranspiration (11.6%). The findings also demonstrate that blue evapotranspiration predominantly occurs during dry months, indicating heavy reliance on stored water for irrigated landscapes. Subbasin-level analyses showed varying blue/green evapotranspiration patterns based on precipitation and irrigated agriculture. Irrigation accounted for less than 3% of the streamflow in upstream basins, while midstream and downstream basins utilized up to 30 and 70%, respectively. The complementary use of rainfall and irrigation in most parts of the basin is considered to be of interest to water managers.