Journal of Hydrology X最新文献

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.

In this work, we explore the statistical behavior of one of the longest rainfall time-series in Italy and in the world, covering the period 1782–2017. Some standard and innovative statistical tools are applied to test the variability and change of the process across all values (in average, but also in terms of extremes) and scales (from days to years). An oscillation pattern occurs across all the time scales, from years to decades, limited by the sample length. It implies that there are no particular periods of variability, apart from seasonality, and no statistically significant trends, such that the process can be fully characterized in terms of the Hurst coefficient. Despite its exceptional length, the dataset is still insufficient to adequately capture the complex behavior of rainfall over the time scales, especially with regards to extremes, and to separate anthropogenically induced change from natural variability based on the data alone. Our findings suggest that samples of limited length do not allow robust statistical predictions, raising concerns about statistical analyses based on a limited dataset, even a relatively large one.

Over the past century, water temperatures in many streams across the Pacific Northwest (PNW) have steadily risen, shrinking endangered salmonid habitats. The warming of PNW stream reaches can be further accelerated by wildfires burning forest stands that provide shade to streams. However, previous research on the effect of wildfires on stream water temperatures has focused on individual streams or burn events, limiting our understanding of the diversity in post-fire thermal responses across PNW streams. To bridge this knowledge gap, we assessed the impact of wildfires on daily summer water temperatures across 31 PNW stream sites, where 10–100% of their riparian area burned. To ensure robustness of our results, we employed multiple approaches to characterize and quantify fire effects on post-fire stream water temperature changes.

Averaged across the 31 burned sites, wildfires corresponded to a 0.3 – 1°C increase in daily summer water temperatures over the subsequent three years. Nonetheless, post-fire summer thermal responses displayed extensive heterogeneity across burned sites where the likelihood and rate of a post-fire summer water temperature warming was higher for stream sites with greater proportion of their riparian area burned under high severity. Also, watershed features such as basin area, post-fire weather, bedrock permeability, pre-fire riparian forest cover, and winter snowpack depth were identified as strong predictors of the post-fire summer water temperature responses across burned sites. Our study offers a multi-site perspective on the effect of wildfires on summer stream temperatures in the PNW, providing insights that can inform freshwater management efforts beyond individual streams and basins.

This study examined recent advances in remote sensing (RS) techniques used for the quantitative monitoring of groundwater storage changes and assessed their current capabilities and limitations. The evolution of the techniques analyses spans from empirical reliance on sparse point data to the assimilation of multi-platform satellite measurements using sophisticated machine learning algorithms. Key developments reveal enhanced characterisation of localised groundwater measurement by integrating coarse-resolution gravity data with high-resolution ground motion observations from radar imagery. Notable advances include improved accuracy achieved by integrating Gravity Recovery and Climate Experiment (GRACE) and Interferometric Synthetic Aperture Radar (InSAR) data. Cloud computing now facilitates intensive analysis of large geospatial datasets to address groundwater quantification challenges. While significant progress has been made, ongoing constraints include coarse spatial and temporal resolutions limiting basin-scale utility, propagation of uncertainties from sensor calibrations and data merging, and a lack of systematic validation impeding operational readiness. Addressing these limitations is critical for continued improvement of groundwater monitoring techniques. This review identifies promising pathways to overcome these limitations, emphasising standardised fusion frameworks for satellite gravimetry, radar interferometry, and hydrogeophysical techniques. The development of robust cloud-based modelling platforms for multi-source subsurface information assimilation is a key recommendation, highlighting the potential to significantly advance groundwater quantification accuracy. This comprehensive review serves as a valuable resource for water resource and remote sensing experts, providing insights into the evolving landscape of methodologies and paving the way for future advancements in groundwater storage monitoring tools.

Snow depth (SD) is a crucial variable of the water, energy, and nutrient cycles, impacting water quantity and quality, the occurrence of floods and droughts, snow-related hazards, and sub-surface ecological functions. As a result, quantifying SD dynamics is crucial for several scientific and practical applications. Ground measurements of SD provide information at sparse locations, and physical global model simulations provide information at relatively coarse spatial resolutions. An approach to complement this information is using stochastic models that generate time series of hydroclimatic variables, preserving their statistical properties in a computationally-effective manner. However, stochastic generation methods to produce SD time series exclusively do not exist in the literature. Here, we apply a stochastic model to produce synthetic daily SD time series trained by 448 stations in Canada. We show that the model captures key statistical properties of the observed records, including the daily distributions of zero and non-zero SD, temporal clustering (i.e., autocorrelation), and seasonal patterns. The model also excelled in capturing the observed higher-order L-moments at multiple temporal scales, with biases between simulated and observed L-skewness and L-kurtosis within ($-$0.1, +0.1) for 93.0 % and 98.3 % of the stations, respectively. The stochastic modelling approach introduced here advances the generation of SD time series, which are needed to develope Earth-system models and assess the risk of snowmelt flooding that lead to severe damage and fatalities.

Agriculture in regions with limited water availability is possible because of irrigation. Irrigated croplands are expanding, and irrigation water demand is increasing. Nevertheless, there is a limited understanding of how much water is consumed for irrigation and how effective irrigation increases crop productivity in various climates. In this study, we aim to understand how irrigation water affects crop productivity in different climates. To achieve this goal, we developed a simple approach to quantify irrigation quantities from SMAP satellite soil moisture observations based on a zero-dimensional bucket-type hydrology model. The central assumption is that irrigation quantities can be estimated from the gap between the modeled and observed soil moisture by iteratively providing irrigation as a model input until the soil moisture simulations agree well with the observations. We then used the estimated amount of irrigation to simulate water, energy, and carbon fluxes at two agricultural sites on the west coast of the US: one that was water-limited (Central Valley, CA) and one that was energy-limited (Eugene, OR). An agroecosystem model, AgroIBIS-VSF, was used to conduct simulations. To verify our simulations, we used data from two AmeriFlux Eddy covariance towers at each site. We found that incorporating estimated irrigation amounts into our simulations improved the accuracy of energy balance components and soil moisture predictions, reducing the root-mean-square error of soil moisture predictions by up to 22%. We also discovered that the irrigation value, in terms of increased productivity of actual irrigation water used, is more than five times more valuable at the energy-limited site than at the water-limited site. Soil hydraulic properties have a strong influence on irrigation water valuation. Our study highlights the potential of satellite soil moisture observations to improve our understanding of water productivity in different climates. By better understanding the efficiency of resources used for crop production, we can ensure the sustainability and resilience of agricultural systems, leading to better management practices.

The increase in extreme rainfall events due to climate change, combined with urbanisation, leads to increased risks to urban infrastructure and human life. Physically based urban flood models capable of producing water depth maps with sufficient spatial and temporal resolution are generally too slow for decision makers to react in time during an extreme event. We present a surrogate model with high temporal and spatial resolution for real-time prediction of water levels during a pluvial urban flood. We used machine learning techniques to achieve short computation times. The recursive approach used in this work combines convolutional and fully coupled multilayer architectures. The database for the machine learning was pre-simulated results from a physically based urban flood model. The forcing input of the prediction is precipitation and the output is water level maps with a temporal resolution of 5 min and a spatial resolution of 6 x 6 meters. The prediction performance can be considered promising for testing the model in real operational applications.

Instream temperatures control numerous biophysical processes and are frequently the subject of modeling efforts to understand and predict responses to watershed conditions, habitat alterations, and climate change. Air temperature (AT) is regularly used in statistical temperature models as a covariate proxy for physical processes and because it correlates strongly with spatiotemporal variability in water temperatures (T_w). Air temperature data are broadly available and sourced from sensors paired with T_w sites, remote weather stations, and gridded climate data sets—often with limited recognition of the tradeoffs these sources present and how microclimatic variation in topographically complex mountain environments could affect model inference. To address these issues, we collected daily T_w records at 13 sites throughout a mountain river network, linked the records to AT data from 11 sources available across much of North America, and fit linear regression models to assess predictive performance and the consistency of parameter estimation. Although the predictive accuracy of these models was generally high, estimates of the AT slope parameter, which is commonly interpreted as thermal sensitivity, varied substantially depending on the AT data source. These results have implications for the comparability of estimates among T_w studies and highlight the challenges that modeling stream temperatures in mountain landscapes presents. Although no AT data source is ideal, some are more advantageous than others for specific use cases and we provide general recommendations on this topic.