National Institute of Meteorology

Extreme rainfall events can trigger flash floods that pose serious risks to communities, infrastructure, and critical services, particularly in arid and rapidly urbanizing environments. In the Kingdom of Saudi Arabia, short hydrological response times, strong spatial variability of precipitation, complex topography, and limited observational data significantly challenge flood early warning capabilities, which affect emergency management at the national scale. Addressing these challenges requires integrated and scalable hydro-meteorological forecasting systems capable of operating across large spatial domains while resolving convective weather events and associated localized flood impacts in urban/suburban areas.This study presents a nationwide, operational flash flood early warning system developed for the Kingdom of Saudi Arabia. The system is designed to provide consistent coverage across the country while capturing fine-scale weather, hydrological and hydrodynamic processes relevant to flash flooding in arid environments. It operates over 137 hydrological domains, representing more than 6,000 outlets, delivering 2D flood simulations at a spatial resolution of 30 m nationwide, with enhanced resolution of up to 2.5 m in selected urban areas.The forecasting framework is structured as an end-to-end modeling chain that links atmospheric forcing, hydrological response, hydraulic flood propagation, and infrastructure impacts. High-resolution numerical weather predictions generated by the Weather Research and Forecasting (WRF) model are combined with real-time radar and rain gauge observations to produce hourly ensemble weather and precipitation forecasts and hindcasts. These meteorological inputs drive a distributed hydrological model (CREST), which simulates runoff generation across arid catchments using spatially explicit information on topography, land cover, soil properties, and drainage networks. A reservoir management module is fully integrated within the modeling chain, allowing the system to account for reservoir storage dynamics, controlled releases, and spillway operations, and to assess the influence of dam infrastructure on downstream flood evolution.Hydrological outputs are used as boundary conditions to a two-dimensional hydrodynamic model, which simulates floodplain dynamics, water depths, and inundation extents.All model components are coupled within a WebGIS-based operational platform that displays deterministic and ensemble weather and hydrologic forecasts, probabilistic flood warnings, and real-time nowcasting products. Flood hazard information is delivered through interactive maps, warning levels, and time series, to support decision- making by civil protection authorities and emergency managers at national and local scales.The functionality and operational performance of the system are demonstrated through its application on a recent extreme rainfall and flash flood events that affected the entire region of Saudi Arabia in the period of December 9-16, 2025. The system successfully captured the timing, spatial extent, and severity of flooding across multiple domains, providing useful lead times and high-resolution inundation maps. This case study highlights the robustness, scalability, and operational value of the framework, demonstrating its potential to enhance flood preparedness through early warning, and risk management across the Kingdom of Saudi Arabia under increasing hydro- meteorological extremes.

Urban flooding, increasingly aggravated by climate change and unplanned urban expansion, poses multifaceted risks to infrastructure and heightens public-health vulnerability by amplifying infectious-disease transmission. Beyond physical damage, floodwaters transport untreated sewage, industrial effluents, and microbial contaminants, substantially increasing human exposure and accelerating waterborne disease spread. These cascading exposure pathways remain insufficiently quantified in rapidly urbanizing and climate-vulnerable settings, underscoring the need for an integrated assessment of the hydrology–health nexus. In this study, we examine the convergence of flood hazards and human-health risks along the Yamuna River corridor in Delhi, a megacity where extreme rainfall, recurrent urban flooding, informal settlements, and stressed sanitation systems collectively heighten vulnerability, conditions expected to intensify under future climate and socio-economic scenarios. We develop an integrated modelling chain that links climate-forced hydrological simulations, coupled urban flood modelling, contaminant transport, and Quantitative Microbial Risk Assessment (QMRA). A semi-distributed SWAT model, driven by grid-wise selected and DQM bias-corrected NEX-GDDP-CMIP6 forcings, simulates future streamflow for the Upper Yamuna Basin under SSP2-4.5 and SSP5-8.5 scenarios. Design discharges are extracted using a Peaks-Over-Threshold framework with Generalized Pareto modelling, while climate-adjusted design rainfall is generated through a copula-based Depth–Duration–Frequency framework integrating historical statistics with CMIP6 projections. These scenario-specific hydrometeorological forcings drive a fully coupled process-based MIKE+ hydrodynamic model to simulate future changes in flood extent, depth, and flow pathways across Delhi’s complex urban terrain. Hydrodynamic outputs feed into the MIKE ECO Lab module to simulate the transport and fate of faecal indicator bacteria (E. coli), and infection risks are quantified using a β-Poisson dose–response model. By integrating hydrological extremes, contaminant transport, climate projections, and exposure pathways, this study provides new insight into cascading flood–disease interactions in urban environments. The results show that the climate-driven increases in extreme rainfall and flood magnitude may exacerbate public-health risks and spatial inequities, challenging emergency response and risk-reduction capacities. The framework is transferable to other hazard-prone settings and offers a basis for developing integrated multi-hazard risk-reduction strategies.Keywords: Hydrology–health nexus; multi-hazard modelling; urban flooding; climate change; human-health risk

Ground-based profilers provide continuous information on atmospheric boundary-layer (ABL) temperature and humidity, but zenith-only observations suffer from large representation errors in heterogeneous environments. This contribution explores the potential of scanning Microwave Radiometer (MWR) brightness temperatures (TBs) to better constrain ABL water vapor and to reduce representation error relevant for convection-permitting data assimilation. It especially aims to eventually evaluate the synergy of scanning MWR humidity observations with the already planned Differential Absorption Lidar (DIAL) network LIDIA by DWD.As a proof of concept, radiosonde profiles are combined with co-located ground-based HATPRO MWR observations from recent field campaigns in Germany, including FESSTVaL (2021), Socles (2021–2022), and Vital I (2024). For each radiosonde launch, temporally matched MWR measurements are extracted for several viewing geometries. The evaluation by TB forward modeled from radiosondes gives first promising results.The presentation highlights how low elevation azimuth scan TB information, especially combined with the upcoming LIDIA network can provide additional constraints on horizontal gradients and boundary layer humidity. The next steps are: assimilation experiments with data from the upcoming Vital II campaign (summer 2026), where combined zenith-pointing DIAL and scanning MWR observations will be assimilated into ICON-D2 to quantify their impact on short-range forecasts of humidity and convective initiation on convection-permitting resolution.

Freshwater reservoir drawdown areas are an important yet not well understood source of atmospheric CO2. Quantifying CO2 emissions from drawdown areas is challenged by considerable spatial and temporal variability. In this study we aimed to assess temporal variability to understand the drivers of drawdown area CO2 emissions and to improve reservoir CO2 emission budgets. We measured CO2 emissions from the drawdown area of a German reservoir for three months with hourly resolution using automatic flux chambers. To analyze drivers of CO2 emissions we monitored sediment temperature and moisture as well as meteorological variables. For comparison we continuously measured CO2 emissions from the water surface by Eddy Covariance. CO2 fluxes showed pronounced diurnal and seasonal variability driven by temperature. Sediment moisture had a negative effect on CO2 fluxes – low fluxes were observed close to the waterline and after rain events. Inhibition of CO2 transport by water blocking sediment pores was indicated by a hysteresis between sediment temperature and CO2 flux, which was amplified under moist conditions. We show that daily CO2 emissions are overestimated by more than 25% if fluxes are measured only during midday. A comparison with CO2 emissions from the water surface measured by eddy covariance revealed that drawdown area and water surface contributed equally to the reservoirs CO2 emissions, despite the much small surface area of the drawdown area. Our study clearly shows that CO2 fluxes between reservoir drawdown area and atmosphere have a large diurnal variability in the same range as seasonal variability and within system variability. High temporal resolution measurements of CO2 emissions from the drawdown area are essential for precise quantification of greenhouse gas emissions from reservoirs.

In the Indo-Burma subduction zone the subducted and hanging-wall lithospheres interact strongly with the asthenosphere due to India-Asia plate convergence. Imaging the lithosphere-asthenosphere boundary (LAB) is therefore crucial for an in-depth understanding of the convergence process. Our Sp receiver function (RF) images built with dense array data reveal a regionally shallow LAB at an average depth of ~70 km, except beneath the Indo-Burma Ranges, where the Indian LAB dips eastward at an average of ~20° down to ~140 km; this dipping geometry is resolved primarily through SKS-derived RFs. Farther east, a positive velocity gradient emerges ~50-80 km beneath the Burma LAB, together they define an upper-asthenospheric low-velocity layer. Waveform modeling further indicates a sharp LAB, characterized by a Vs drop up to ~6-10% over <~20 km. Our RF results support a melt-rich LAB, potentially linked to the asthenospheric flow induced by the Indian plate subduction and subsequent rollback and/or tearing.

Globally, impacts of climate change observed across the sources of water, agricultural planning, and socio-economic factors. Objectives of the study are to analyze historical climate data, focusing on rainfall and temperature trends over the period 1981–2014 and to project future climate scenarios under SSP2-4.5 and SSP5-8.5 for the periods 2015–2040 (near term), 2041–2070 (mid-term), and 2071–2100 (end term), assessing changes in seasonal and annual rainfall and temperature. This study investigates historical and projected climate trends in Tiyo Woreda, Arsi Zone, Ethiopia, using data sources Copernicus and ECMWF and CMIP6 climate models under SSP2-4.5 and SSP5-8.5 scenarios. Ensembles global climate models selected from the five Coupled Model Intercomparison Project (CMIP6) are considered under SSP2-4.5 and SSP5-8.5 socio-economic pathways. The research conducted using multiple software tools, such as Excel for data entry and manipulation, Python for data processing, visualization, Heatmaps and plotting, and ArcGIS and GeoCLIM for location of study area and spatial analysis. The result demonstrated historical climate rainfall is highest during the Kiremt, moderate during Belg, and lowest during Bega. Projections both SSP2-4.5 and SSP5-8.5 scenarios suggest that rainfall will generally increase in the mid- and long term, though slight reductions are observed in the near term compared to the historical baseline. Seasonal trends of Bega and Belg rainfall show small near-term declines and slight increase respectively but recover and increase in later periods. Kiremt rainfall exhibits modest but consistent increases, with higher values under SSP5-8.5. Annual rainfall follows a similar trajectory, with gradual increases across the projection horizon. Scenario comparison across SSP5-8.5 consistently projects greater increases in rainfall than SSP2-4.5, particularly in the mid- to long-term. In general historical analysis show mixed rainfall trends with increases in Kiremt and decreases in Bega and Belg, while minimum and maximum temperatures exhibit a warming trend across all seasons. CMIP6 projections indicate an overall increase in seasonal rainfall and temperature, with more pronounced warming under SSP5-8.5. Projections SSP2-4.5 and SSP5-8.5 scenarios of maximum temperature show an increment across the periods of near-, mid- and end-term with the magnitude of 0.7°C, 1.6°C, 2.1°C and 0.8°C, 1.9°C, 3.6°C respectively. These results highlight the need for adaptive strategies in agricultural planning and water resource management to mitigate the impacts of climate change in the Tiyo Woreda.

The seasonal dynamics, sources, and ecological risks of polycyclic aromatic hydrocarbons (PAHs) in inland freshwater ports remain largely limited, despite extensive research on coastal port PAH pollution. Here, we investigated sixteen U.S. EPA priority PAHs in surface waters of Jiujiang Port, a major inland hub along the Yangtze River, China. Total PAH concentrations ranged from 21.8 to 121.0 ng·L−1 (mean: 65.0 ng L−1), which represents relatively low levels compared with coastal ports worldwide. In this study, significant seasonal variations were also observed, with higher concentrations during the dry season than the wet season. Diagnostic ratios and multivariate analyses indicated petroleum combustion as the dominant source, while PAH levels showed positive correlations with turbidity and CODMn, underscoring the role of suspended particulates and organic load. Ecological risk assessment revealed low to moderate risks, with elevated risks in the dry season. These findings provide novel insights into PAH pollution in inland port systems and offer a scientific basis for pollution control and ecological management under the Yangtze River Protection framework.

Machine learning models have been used with success to produce accurate river discharge forecasts at multiple lead times. However, almost no research has been done to show if they are physically consistent across lead times. In the deterministic probl