Journal of Hydrology X最新文献

The relationship between mean daily precipitation and elevation is often regarded as linear and positive, resulting in simple “precipitation lapse rate” equations frequently employed to extrapolate daily rainfall from a single weather station over a large area. We examine the precipitation-elevation relationship in the Swiss Alps using a combination of weather radar and rain gauge data to test this common assumption, challenging it by fitting a two-segment piecewise linear model with a mid-slope break-point as an alternative. By examining data stratified by catchment, season, and weather type, we assess the space–time variability of the precipitation-elevation relationship. We conclude that a non-linear and non-stationary model seems necessary to capture the variability of the observed precipitation-elevation relationship. Based on our findings, we suggest that the simplified precipitation lapse rate concept is misleading and should be reconsidered in hydrological applications, emphasizing the need for a more realistic representation of precipitation variability over time and space.

Explainable Artificial Intelligence (XAI) offers the promise of being able to provide additional insight into complex hydrological problems. As the “new kid on the block”, these methods are embraced enthusiastically and often viewed as offering something radically new and different. However, upon closer inspection, many XAI approaches are very similar to more “traditional” methods of “interrogating” existing models, such as sensitivity or break-even analysis. In fact, the approach of developing data-driven models to obtain a better understanding of hydrological processes to inform the development of more physics-based models is as old as hydrology itself. Consequently, rather than being considered a new approach, XAI should be viewed as part of a long-standing tradition, and XAI methods part of an ever-expanding hydrological modelling toolkit, rather than a silver bullet. Critically, there needs to be shift from focusing on how to best eXplain what AI models have learnt (i.e., the X component of XAI) to developing models that are able to capture relationships that are contained within the data in a robust and reliable fashion (i.e., the AI component of XAI), as there is little value in explaining AI-derived relationships if these do not reflect underlying hydrological processes. However, this is often not the case due to a focus on maximising the predictive ability of AI models “at all costs”, not uncommonly resulting in large models that often have thousands or even millions of parameters that are not well defined. Consequently, these models generally do not capture underlying hydrological processes in a robust and reliable fashion. Finally, there is also a need to stop thinking about XAI as a purely technical approach, but a socio-technical approach that views XAI as a process that can assist with solving problems that are situated within broader social and political contexts.

This study attempts to statistically characterize the Urban Heat Island Intensity (UHII) ($\Delta T$) for 55 cities under three climate regimes – arid, snow and temperate – across the US. The study uses remotely sensed data products, daily temperature from MODIS and daily evapotranspiration from SSEBop model, to calculate the urban–rural difference in daily-mean temperature and daily-mean evapotranspiration ($\Delta T$ and $\Delta ET$ respectively) for the selected cities. By developing a hierarchical model that explains UHII using temporally-varying $\Delta ET$ and spatially-varying urban morphometric characteristics (total urban area and percentage impervious area) available for each city, we find that 89% of the spatio-temporal variability in annual $\Delta T$ can be explained. The relationship between $\Delta T$ and $\Delta ET$ is found to be negative indicating increased difference in daily means of ET ($\Delta ET$) result in increased difference in daily means of temperature ($\Delta T$) between urban and rural paracels The variation of $\Delta T$ per unit $\Delta ET$ is found to be highest in arid and snowy environments and smallest in temperate environments in the south-southeast US. The relation between $\Delta T$ and $\Delta ET$ is negative for most cities, except Madison (WI) and Sacramento (CA), across the US. Both the selected urban morphometric properties are found to be statistically significant in explaining the spatial variability in UHII, but the difference in urban–rural difference in evapotranspiration is the primary driver for UHII.

Flood early warning systems (FEWS) are effective means for saving human lives from the devastating impacts of extreme hydrological events. FEWS relies on hydrologic monitoring networks that are typically expensive and challenging to design. This issue is particularly relevant when identifying the most cost-efficient number, type, and positioning of the sensors for FEWS that may be used to take decisions and alert the population at flood risk.

In this study, we focus on a widely recognized FEWS solution to analyze hydrological monitoring and forecasting performances expressed as discharge in various cross-sections of a drainage network. We propose and test a novel framework that aims to maximize FEWS performances while minimizing the number of sections that need instrumentation and suggesting optimal sensor placement to enhance forecasting accuracy. In the selected case study, we demonstrate through feature importance measure that only four sub-basins can achieve the same forecasting performance as the potential twenty-six cross-sections of the local hydrologic monitoring network. The operational dashboard resulting from our proposed framework can assist decision-makers in maximizing the performance and wider adoption of flood early warning systems across geographic and socio-economic scales.

While sub-daily precipitation extremes cause flash flooding and pose risk to life, longer precipitation extremes threaten infrastructure such as water supply dams. Frequent storm or floods events replenish water supplies, ensuring the health of our ecosystems, while rarer larger storms or floods cause damage to property and life. These differing impacts depend on both storm rarity and duration and are largely dependent on coincident atmospheric water vapour. Using a novel metric that quantifies the extent of concurrency that exists between precipitation and total water vapour extremes, large regional variations are identified across the globe. Tropical regions such as Northeast Africa and South/East Asia consistently exhibit greater concurrency across all precipitation durations. In contrast, areas of the extra-tropics, such as the Mediterranean and Northwest Americas, show a rapid decline in concurrency with increasing duration. However, for rare events of long duration, non-tropical regions maintain high concurrency. With the link between climate change and increasing total water vapour well established, these results suggest that flood events will increase globally, with increases most apparent for longer and rarer events. This work underscores the need for tailored regional strategies in managing extreme precipitation and flood events in the future.

The magnitude and timing of extreme events such as debris and floodflows (collectively referred to as floodflows) in post-wildfire basins are difficult to measure and are even more difficult to predict. To address this challenge, a sensor ensemble consisting of noncontact, ground-based (near-field), Doppler velocity (velocity) and pulsed (stage or gage height) radars, rain gages, and a redundant radio communication network was leveraged to monitor flood wave velocities, to validate travel times, and to compliment observations from NEXRAD weather radar. The sensor ensemble (DEbris and Floodflow Early warNing System, DEFENS) was deployed in Waldo Canyon, Pike National Forest, Colorado, USA, which was burned entirely (100 percent burned) by the Waldo Canyon fire during the summer of 2012 (MTBS, 2020).

Surface velocity, stage, and precipitation time series collected during the DEFENS deployment on 10 August 2015 were used to monitor and predict flood wave velocities and travel times as a function of stream discharge (discharge; streamflow). The 10 August 2015 event exhibited spatial and temporal variations in rainfall intensity and duration that resulted in a discharge equal to 5.01 cubic meters per second (m³/s). Discharge was estimated post-event using a slope-conveyance indirect discharge method and was verified using velocity radars and the probability concept algorithm. Mean flood wave velocities – represented by the kinematic celerity $\left(c_k=2.619meterspersecond,m/s\pm 0.556percent\right)$ and dynamic celerity $\left(c_d=3.533m/s\pm 0.181percent\right)andtheiruncertainties$ were computed. L-moments were computed to establish probability density functions (PDFs) and associated statistics for each of the at-a-section hydraulic parameters to serve as a workflow for implementing alert networks in hydrologically similar basins that lack data.

Measured flood wave velocities and travel times agreed well with predicted values. Absolute percent differences between predicted and measured flood wave velocities ranged from 1.6 percent to 49 percent