Journal of Hydrology X最新文献

Hillslope aquifers regulate streamflow and are a critical potable and irrigation water source, especially in developing countries. Knowing recharge and baseflow is essential for managing these aquifers. Methods using available data to calculate recharge and baseflow from aquifers are not valid for uplands. This paper adapts the Thornthwaite and Mather (T-M) procedure from plains to sloping and mountainous regions by replacing the linear reservoir with a zero-order aquifer. The revised T-M procedure was tested over four years in two contrasting watersheds in the humid Ethiopian highlands: the 57 km² Dangishta with a perennial stream and the nine km² Robit Bata, where the flow ceased four months after the end of the rain phase. The monthly average groundwater tables were predicted with an accuracy ranging from satisfactory to good for both watersheds. Baseflow predictions were “very good” after considering the evaporation from shallow groundwater in the valley bottom during the dry phase in Dangishta. We conclude that the T-M procedure is ideally suited for calculating recharge, baseflow and groundwater storage in upland regions with sparse hydrological data since the procedure uses as input only rainfall and potential evaporation data that are readily available together with an estimate of the aquifer travel time.

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.

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.

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.

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.

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.

Tile drains constitute a shortcut from agricultural fields to surface water systems, significantly altering the transport pathways and fate of nitrate during transport. A correct representation of tile drainage flow is thus crucial for estimating nitrate load at the catchment scale and to identify optimal locations for N-mitigation measures. Drainage is a local process, controlled by local properties and drain configurations, which are rarely known for individual fields, making drainage flow and transport a challenging task in catchment scale models. This study tests the potential for improving drainage flow dynamics at catchment scale, by utilising local drainage flow measurements in a spatial calibration scheme. A distributed hydrological model, MIKE SHE, for the agricultural-dominated Norsminde catchment (145 km²) in Denmark, was calibrated using spatially distributed surrogate parameters (pilot points) to represent heterogeneity in the soil (top 3 m) and the deeper geology below 3 m. The model was calibrated using hydraulic heads, stream discharge, and measured drainage flow from eight drain catchments. Drain measurements were very important in guiding the calibration of top 3 m and subsurface pilot points located in the drainage fields, showing that drain flow hold information on both local (shallow) and regional (deeper) flow patterns. Contrarily, pilot points located outside the drained fields were mainly sensitive to the hydraulic head measurements and the summer water balance of the stream discharge on a catchment scale. Consequently, incorporation of the drain data improved local performance, but did not improve the parameterization and drain description of the entire catchment. Exploitation of the drain flow information is thus difficult beyond the drain catchments, and other approaches are needed to extrapolate and exploit the local data.

Standard flood frequency analysis assumes stationarity of flood conditions, i.e., no change of the distribution over time. However, long-term variability in climate and anthropogenic impacts question this assumption. Consequently, more and more non-stationary models are considered in flood frequency analyses. Yet, most of them only consider a change-point or trend in the magnitude of flood peaks while ignoring changes in the underlying flood geneses. Recent climate reports suggest such a change in frequency of certain flood-generating factors, e.g., the increase of frequency of heavy-rainfall events. In this study, flood types are applied to detect changes in the meteorological drivers of flood regimes. By application of a robust change-point test for the variance based on Gini’s Mean Difference, significant changes in the frequency of occurrence of certain flood types are detected. A clear tendency to more frequent heavy-rainfall floods and less snowmelt-induced floods is observed for many catchments in Central Europe. A special focus is laid on the shifts in winter floods, which occur less often and are replaced by rainfall-driven floods. The impacts of such changes on flood statistics are demonstrated by several approaches. Though the magnitude of flood peaks does not (necessarily) change, the changing frequency of floods leads to changing flood quantiles. Quantile estimations from traditional statistical analyses of annual series are compared to results of type-based flood statistics. It is shown how standard models are more affected by these changes because they are not able to compensate for changes in the frequency of individual flood types.

This paper presents a methodology for the creation of dynamic reservoir rule curves on the basis of the results of implicit stochastic optimization coupled with optimized demand hedging embedded as constraints to optimization. The novelty of the method is a dynamic rule curve that always starts from the current storage level and projects a range of anticipated target levels in the immediate future based on the statistical analyses of the results of implicit stochastic optimization. The method is particularly useful in dry years when storage is not completely filled at the end of wet seasons. Such situations cannot be addressed with standard traditional rule curves, thus causing reservoir operators to base their decisions on mere judgment. The proposed method can be helpful in such situations. The method has been demonstrated on the Tawa reservoir in the Narmada River Basin in India.

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.