Abstract. Hybrid hydroclimatic forecasting systems employ data-driven (statistical or machine learning) methods to harness and integrate a broad variety of predictions from dynamical, physics-based models – such as numerical weather prediction, climate, land, hydrology, Earth system into final prediction product. They are recognized promising way enhancing the skill meteorological variables events, including rainfall, temperature, streamflow, floods, droughts, tropical cyclones, atmospheric...

Accelerated melting of glaciers is expected to have a negative effect on the water resources mountain regions and their adjacent lowlands, with tropical being among most vulnerable. In order quantify those impacts, it necessary understand changing dynamics glacial melting, but also map how meltwater contributes current future use, which often occurs at considerable distance downstream terminus glacier. While melt are increasingly well understood documented, major uncertainty remains...

Abstract Explaining the spatially variable impacts of flood‐generating mechanisms is a longstanding challenge in hydrology, with increasing and decreasing temporal flood trends often found close regional proximity. Here, we develop machine learning‐informed approach to unravel drivers seasonal magnitude explain spatial variability their effects temperate climate. We employ 11 observed meteorological land cover (LC) time series variables alongside 8 static catchment attributes model 1,268...

Sub-Saharan Africa (SSA) is strongly affected by flood hazards, endangering human lives and economic stability. However, the role of internal climate modes variability in driving fluctuations SSA occurrence remains poorly documented understood. To address this gap, we quantify relative combined contribution large-scale drivers to seasonal regional using a new 65-year daily streamflow dataset, sea-surface temperatures derived from observations, 12 Single Model Initial-condition Large...

Abstract. We present the lulcc software package, an object-oriented framework for land use change modelling written in R programming language. The contribution of work is to resolve following limitations associated with current paradigm: (1) source code model implementations frequently unavailable, severely compromising reproducibility scientific results and making it impossible members community improve or adapt models their own purposes; (2) ensemble experiments capture structural...

The exposure of urban populations to flooding is highly heterogeneous, with the negative impacts experienced disproportionately by poor. In developing countries experiencing rapid urbanization and population growth a key distinction in landscape between planned development unplanned, informal development, which often occurs on marginal, flood-prone land. Flood risk management context informality challenging, may exacerbate existing social inequalities entrench poverty. Here, we adapt an...

Abstract. Hybrid hydroclimatic forecasting systems employ data-driven (statistical or machine learning) methods to harness and integrate a broad variety of predictions from dynamical, physics-based models – such as numerical weather prediction, climate, land, hydrology Earth system into final prediction product. They are recognised promising way enhancing skill meteorological variables events, including rainfall, temperature, streamflow, floods, droughts, tropical cyclones, atmospheric...

Abstract Water logging is one of the most detrimental phenomena continuing to burden Dhaka dwellers. This study aims spatio-temporarily identify water hazard zones within Metropolitan area and assess extent their susceptibility based on informal settlements, built-up areas, demographical characteristics. The utilizes integrated geographic information system (GIS)-remote sensing (RS) methods, using Normalized Difference Vegetation Moisture Index, distance buffer zone from drainage streams,...

Abstract Almost 2 billion people depend on freshwater provided by the Asian water towers, yet long‐term runoff estimation is challenging in this high‐mountain region with a harsh environment and scarce observations. Most hydrologic models rely observed for calibration, have limited applicability poorly gauged towers. To overcome such limitations, here we propose novel data‐driven model, SM2R (Soil Moisture to Runoff), simulate monthly based soil moisture dynamics using reanalysis forcing...

Abstract Understanding drivers of river mobility—temporal shifts in channel positions—is critical for managing fluvial landscapes sustainably and interpreting past responses to climate change. However, direct observations linking mobility water discharge variability are scarce. Here, we pair multi‐annual measurements daily mobility, estimated from Landsat, 48 rivers worldwide. We show that, across climates planforms, is correlated with over daily, intra‐annual, inter‐annual timescales. For...

Quantifying impacts of land-use change on streamflow extremes is challenging, primarily due to the masking effects other environmental processes. Our current understanding these remains incomplete. Here, we use explainable machine learning techniques analyse over 1.5 million seasonal 7-day low-flow and high-flow events across 10,717 catchments worldwide between 1982 2023. model incorporates antecedent meteorological conditions, annual six categories, catchment characteristics...

River discharge prediction is critical for water resource management, yet equifinality—where multiple model configurations achieve similar accuracy—complicates process understanding. We explored this phenomenon using Long Short-Term Memory (LSTM) models trained on UK river basins, incorporating geomorphic descriptors derived from Digital Terrain Models and other environmental features from the CAMELS-GB dataset, including land cover, soil, climate...

Abstract Understanding water user behavior and its potential outcomes is important for the development of suitable resource management options. Computational models are commonly used to assist decision making; however, while natural processes increasingly well modeled, inclusion human has lagged behind. Improved representation irrigation within can provide more accurate relevant information in agricultural sector. This paper outlines a model that conceptualizes proceduralizes observed farmer...

Understanding the drivers of river mobility - temporal shifts in channel positions is critical for managing fluvial landscapes sustainably and interpreting past response to climate change. However, direct observations linking water discharge variability are scarce. To resolve this challenge, we pair multi-annual measurements daily with mobility, estimated from Landsat, 48 rivers worldwide. Our results show that, across climates planforms, correlated over daily, intra-annual, inter-annual...

The estimation, attribution or projection of hydro-meteorological extremes in individual locations is constrained by the limited number observations extreme events. Recent advances large-sample machine learning (ML) models, however, have demonstrated significant potential to mitigate impact data scarcity on quantification hydrological risks. These models integrate hundreds thousands time-series records alongside local descriptors climate and catchment characteristics, enabling them learn...

The consumption of packaged water in Ghana has grown significantly recent years. By 2017, “sachet water”—machine-sealed 500ml plastic bags drinking water—was consumed by 33% Ghanaian households. Reliance on sachet previously been associated with the urban poor, yet evidence suggests a customer base which crosses socioeconomic lines. Here, we conduct repeated cross-sectional analysis three nationally representative datasets to examine changing demography consumers between 2010 and 2017. Our...

Universal access to safe drinking water is essential population health and well-being, as recognized in the Sustainable Development Goals (SDG). To develop targeted policies which improve urban improved ensure equity, there need understand spatial heterogeneity sources factors underlying these patterns. Using Shannon Entropy Index of Concentration at Extremes enumeration area level, we analyzed census data examine neighborhood income Greater Accra Metropolitan Area (GAMA), largest...

The green revolution represents one of the greatest environmental changes in India over last century. Upper Ganges (UG) basin is experiencing rapid rates change land cover and irrigation practices. In this study, we investigated historical rate created future scenario projections by means 30 m-resolution multi-temporal Landsat 5 Thematic Mapper 7 Enhanced Plus data UG basin. Post-classification analysis methods were applied to images order detect quantify land-cover Subsequently, Markov...

In recent decades India has undergone substantial land use/land cover change as a result of population growth and economic development. Historical maps are necessary to quantify the impact at global regional scales, improve predictions about quantity location future support planning decisions. Here, use model driven by district-level inventory data is used generate an annual time series high-resolution gridded for Indian subcontinent between 1960-2010. The allocation procedure based on...

Cities in the developing world are expanding rapidly, and undergoing changes to their roads, buildings, vegetation, other land use characteristics. Timely data needed ensure that urban change enhances health, wellbeing sustainability. We present evaluate a novel unsupervised deep clustering method classify characterise complex multidimensional built natural environments of cities into interpretable clusters using high-resolution satellite images. applied our approach (0.3 m/pixel) image...

Abstract Accurate long‐term flood predictions are increasingly needed for risk management in a changing climate, but hindered by the underestimation of climate variability models. Here, we drive statistical model with large ensemble dynamical CMIP5‐6 precipitation and temperature. Predictions UK winter flooding (95th streamflow percentile) have low skill when using raw 676‐member averaged over lead times 2–5 years from initialization date. Sub‐selecting 20 members that adequately represent...

Abstract Drought early warning systems (DEWSs) aim to spatially monitor and forecast risk of water shortage inform early, risk‐mitigating interventions. However, due the scarcity in situ monitoring groundwater‐dependent arid zones, spatial drought exposure is inferred using maps satellite‐based indicators such as rainfall anomalies, soil moisture, vegetation indices. On local scale, these coarse‐resolution proxy provide a poor inference groundwater availability. The improving affordability...

Abstract The effects of anthropogenic water use play a significant role in determining the hydrological cycle north India. This paper explores impacts within region's regime by explicitly including observed human behaviour, irrigation infrastructure and natural environment CHANSE (Coupled Human And Natural Systems Environment) socio‐hydrological modelling framework. model is constrained qualitative quantitative information collected study area, along with climate socio‐economic variables...