Journal of Hydrology X最新文献

Hydrologic models are a powerful tool to predict water-related natural hazards. Of all hydrologic models, CREST (Coupled Routing and Excess STorage) was developed to facilitate hydrologic sciences and applications across various spatial and temporal scales. The CREST model was the earliest implementation of a quasi-global flood model integrating remote-sensing data and is the first operational deployment of a real-time model in the National Weather Service functioning at flash flood scales across a continent. Since being published in 2011, the CREST model has been evolving to empower flood predictions and to inform water resources management practices. Moreover, the CREST model is convenient to couple with other models/schemes (e.g., weather forecast model, snowmelt model, land surface model, hydrodynamic model, groundwater model, landslide model, vector-based routing) for border practices of investigating water-related natural hazards. To date its 10th anniversary, more than 80 peer-reviewed journal articles that have used the CREST model are curated and reviewed from the aspects of model development, worldwide applications, and outreach to emerging regions. Finally, the future directions for the CREST model family are outlined in the hope of stimulating new research endeavors. A digital collection of CREST model family is archived online at https://crest-family.readthedocs.io/en/latest/.

Based on Feminist Institutionalism, this paper analyses the reasons for gender disbalance in water diplomacy. To this end, it looks at three intergovernmental decision-making forums on shared waters, namely the Nile Technical Advisory Committee, the Chu-Talas Water Commission, and the International Commission for the Protection of the Rhine. The perceived key obstacles for women’s access to decision-making positions were disciplinary gender divides that go along with a largely technical approach to water management, the gender division of labour, cultural norms, and perceptions of good leadership. While their relevance differed in the different socio-economic, political and cultural contexts, the overall results show that male dominance in water diplomacy is not only a matter of numerical representation, but enshrined in professional norms and practices.

Evapotranspiration (ET) is critical for ecosystem protection and water services, especially in the mountainous areas of arid and semi-arid watersheds. The lysimeter and Eddy Covariance (EC) methods are widely used for directly measuring ET, but are difficult to install and apply in mountainous areas with complex topography. The commonly used indirect methods for estimating ET, such as the Penman-Monteith (PM) method, present significant challenges in mountainous areas with scarce data. The simple soil water balance (SWB) method, which estimates ET from soil moisture dynamics, is another reliable and simple method for estimating ET. However, a drawback of the original SWB method is that it assumes soil moisture depletion only occurs through ET, ignoring the process of deep percolation. This restriction limits the applicability of the SWB method. In this study, we improve the SWB method (ISWB) by incorporating a deep percolation module into the soil water balance equation. Subsequently, we compare the estimated ET obtained from the ISWB, the Food and Agriculture Organization (FAO)-56 PM, and the Hargreaves-Samani (HS) methods with the observed ET. Results show that the ISWB method for estimating ET performs better when using the soil moisture of the 0–25 cm and below layers, compared to the 0–20 cm and above layers. Meanwhile, there is no significant difference in performance between using the soil moisture of the 0–25 cm layer and the soil layers below 25 cm. In addition, ignoring interception evaporation has an obvious influence on ET estimation using the ISWB. Furthermore, the comparison indicated that the performance of the ISWB method is superior to that of the FAO-56 PM and HS methods in the study areas. Our study shows that the ISWB method has significant potential for ET estimation in data-scarce and topographic-complex mountainous areas.

The NCRS-curve number equation allows calculating the storm runoff from a rainfall event for specific types of land use. It was based on an analysis of direct runoff data using baseflow corrected hydrographs and rainfall. Given this basis, the curve number equation can be derived assuming a constant effective rainfall intensity and a cubic reciprocal function as the instantaneous unit hydrograph. The instantaneous unit hydrograph and the resulting curve number equation are further generalized by adding a lag time. The equation for a curve number related hydrograph is presented, allowing to fit this curve number-based hydrograph to event data. The curve number itself is shown be a function of a catchment response time and the average event rainfall intensity. As the catchment response time is linked to the time of concentration the curve number equation and the storage index can be linked to catchment- and flow type characteristics. First results suggest that including the rainfall intensity duration frequency function in the curve number equation may explain systematic deviations observed when fitting the NCRS curve number equation to measured data.

As mountainous areas provide abundant water resources to lower elevations, and alpine zones are major recharge areas for water resources, it is important to understand water storage and discharge processes in these zones. Regarding water storage, sedimentary structures (e.g., talus and moraines) in alpine zones function as aquifers. However, the functions of vegetation, thought to contribute to water recharge and storage in forested watersheds, have rarely been investigated. Accordingly, we evaluated the influence of alpine vegetation on water storage processes in alpine zones. Two intensive field surveys were conducted on August 17 and October 5, 2019, in the alpine headwaters of Mt. Norikura in the Northern Japan Alps. Chemical analyses were conducted of rainwater, snowmelt water, and runoff water from bare and vegetated catchments. From the results, a two-component separation was conducted to calculate the contributions of precipitation and groundwater components to runoff water. Our results implied that runoff water from vegetated catchments was in contact with the regolith for longer, with the contribution of groundwater being higher in this runoff water. Moreover, the groundwater component contribution tended to increase as the ratio of vegetation area to bare area in each catchment increased, suggesting a higher water storage function for vegetated areas. In other words, the subsurface water flow should be slower in vegetated areas due to the presence of vegetated soils compared to bare areas where coarse-grained sediments are dominant. Accordingly, the alpine vegetated area has a higher water storage function than the alpine bare area.

The physically-based, spatially-distributed hydrometeorological model SASER, which is based on the SURFEX LSM, is used to model the hydrological cycle in several domains in Spain and southern France. In this study, the modeled streamflows are validated in a domain centered on the Pyrenees mountain range and which includes all the surrounding river basins, including the Ebro and the Adour-Garonne, with a spatial resolution of 2.5 km. Low flows were found to be poorly simulated by the model. We present an improvement of the SASER modeling chain, which introduces a conceptual reservoir, to enhance the representation of the slow component (drainage) in the hydrological response. The reservoir introduces two new empirical parameters. First, the parameters of the conceptual reservoir model were determined on a catchment-by-catchment basis, calibrating against daily observed data from 53 hydrological stations representing near-natural conditions (local calibration). The results show, on the median value, an improvement (ΔKGE of 0.11) with respect to the reference simulation. Furthermore, the relative bias of two low-flow indices were calculated and reported a clear improvement. Secondly, a regionalization approach was used, which links physiographic information with reservoir parameters through linear equations. A genetic algorithm was used to optimize the equation coefficients through the median daily KGE. Cross-validation was used to test the regionalization approach. The median KGE improved from 0.60 (default simulation) to 0.67 (ΔKGE = 0.07) after regionalization and execution of the routing scheme, and 79 % of independent catchments showed improvement. The model with regionalized parameters had a performance, in KGE terms, very close to that of the model with locally calibrated parameters. The key benefit if the regionalization is that allow us to determine the new empirical parameter of the conceptual reservoir in basins where calibration is not possible (ungauged or human-influenced basins).

In global applications and data sparse regions, which comprise most of the earth, hydrologic model-based flood monitoring relies on precipitation data from satellite multisensor precipitation products or numerical weather forecasts. However, these products often exhibit substantial errors during the meteorological conditions that lead to flooding, including extreme rainfall. The propagation of precipitation forcing errors to predicted runoff and streamflow is scale-dependent and requires an understanding of the autocorrelation structure of precipitation errors, since error autocorrelation impacts the accumulation of precipitation errors over space and time in hydrologic models. Previous efforts to account for satellite precipitation uncertainty in hydrologic models have demonstrated the potential for improving streamflow estimates; however, these efforts use satellite precipitation error models that rely heavily on ground reference data such as rain gages or weather radar and do not characterize the nonstationarity of precipitation error autocorrelation structures. This work evaluates a new method, the Space-Time Rainfall Error and Autocorrelation Model (STREAM), which stochastically generates possible true precipitation fields, as input to the Hillslope Link Model to generate ensemble streamflow estimates. Unlike previous error models, STREAM represents the nonstationary and anisotropic autocorrelation structure of satellite precipitation error and does not use any ground reference to do so. Ensemble streamflow predictions are compared with streamflow generated using satellite precipitation fields as well as a radar-gage precipitation dataset during peak flow events. Results demonstrate that this approach to accounting for precipitation uncertainty effectively characterizes the uncertainty in streamflow estimates and reduces the error of predicted streamflow. Streamflow ensembles forced by STREAM improve streamflow prediction nearly to the level obtained using ground-reference forcing data across basin sizes.

Available methods to quantify the recharge of karst aquifers usually rely on spatially and temporally aggregated precipitation measurements and simplified recharge models, employing transfer functions to account for the delay in infiltration and the distribution in time and space. They generally neglect the non-linear nature of infiltration dynamics through the vadose zone, characterized by dual flow behavior with slow diffuse and rapid focused recharge components. Here, we present a methodology that accounts for the physics of flow by employing a variably saturated dual-permeability flow model to simulate diffuse and preferential infiltration in a large-scale carbonate aquifer. The Western Mountain Aquifer (WMA) in Israel and the West Bank was selected as a suitable groundwater basin because of the large thickness of the vadose zone, extending over several hundred-meters, the availability of long-term data as well as the catchment size, stretching across a catchment area of circa 9000km². Together, these characteristics allow the identification and quantification of the spatio-temporal distribution of the infiltration/recharge component, assessed at the level of the groundwater table. The presented methodology allows for improved water resources planning and generalization of the results, i.e., the robustness of large-scale model results with respect to local hydraulic parameter variations and data uncertainty. Semi-arid climate regions with a highly pronounced seasonality of precipitation and intense short-duration rainfalls, such as the Mediterranean region, represent a prime study location because of the clear and pronounced recharge input signals that are not superimposed by summer rainstorms. We simulate the complex dynamics of the dual-domain infiltration and partitioning of the precipitation input signal by employing HydroGeoSphere (HGS) for transient variably saturated water flow. Flow in the limestone rock matrix and high porosity system (i.e., conduits and fractures) is modeled by overlapping individual continua based on the bulk-effective Richards’ equation with van Genuchten (VG) parameters.

Apparent groundwater age dating has been proven useful and robust in understanding water origin and mixing processes, particularly when multiple tracers are considered. However, even though now extensively used, the age tracers have not been widely applied in the general practice of flow and transport model calibration. A multi tracer-study was carried out in the Neogene aquifer in Flanders to quantify the apparent age and construct a joint interpretation for the delineation of different groundwater flow systems. This understanding is critical as part of the safety and feasibility studies for the underlying Boom Clay Formation that has been considered as a potential host rock for the geological disposal of radioactive waste. In this study, we combine evidence from tritium/helium-3 (³H/³He), helium-4 (⁴He) and radiocarbon (¹⁴C) dating as well as stable isotopic (δ¹⁸O, δ²H) and hydrochemical signatures in combination with particle tracking-based age distributions from the 3D groundwater flow model. The results of the study indicate that mixing of groundwater with young and old fractions occurs predominantly in the central part of the aquifer which is made evident by the coexistence of ³H (pre and post-bomb pulse Era), ¹⁴C and ⁴He in several groundwater samples. The mixing between water of different origin is also supported by the sampled stable isotopic and hydrochemical composition of groundwater. Particle tracking residence time results show an acceptable agreement with apparent ages derived from age tracers for young (≤100 years) and old (>1000 years) groundwater. Groundwater with ages between 100 and 1000 years is likely a mixture of water with young/old fractions and shows the strongest discrepancies between advective model ages and age tracer based apparent ages. On the basis of our findings, we distinguish between three groundwater flow systems in the Neogene aquifer: i) a shallow/local flow system, with groundwater originating from modern meteoric water; ii) a deep/semi-regional flow system, characterized by old groundwater where the presence of ⁴He_rad is significant; iii) a mixed zone of groundwater flow where the recently infiltrated meteoric water mixes with discharging old groundwater. These results have helped us to refine previously proposed conceptual models for the study area and will in the end reduce uncertainties relevant to the potential future geological disposal of radioactive waste.

Hydrologic models are robust tools for estimating key parameters in the management of water resources, including water inputs, storage, and pathway fluxes. The selection of process-based versus data-driven modeling structure is an important consideration, particularly as advancements in machine learning yield potential for improved model performance but at the cost of lacking physical analogues. Despite recent advancement, there exists an absence of cross-model comparison of the tradeoffs between process-based and data-driven model types in settings with varying hydrologic controls. In this study, we use physically-based (SWAT), conceptually-based (LUMP), and deep-learning (LSTM) models to simulate hydrologic pathway contributions for a fluvial watershed and a karst basin over a twenty-year period. We find that, while all models are satisfactory, the LSTM model outperformed both the SWAT and LUMP models in simulating total discharge and that the improved performance was more evident in the groundwater-dominated karst system than the surface-dominated fluvial stream. Further, the LSTM model was able to achieve this improved performance with only 10–25% of the observed time-series as training data. Regarding pathways, the LSTM model coupled with a recursive digital filter was able to successfully match the magnitude of process-based estimates of quick, intermediate, and slow flow contributions for both basins (ρ ranging from 0.58 to 0.71). However, the process-based models exhibited more realistic time-fractal scaling of hydrologic flow pathways compared to the LSTM model which, depending on project objectives, presents a potential drawback to the use of machine learning models for some hydrologic applications. This study demonstrates the utility and potential extraction of physical-analogues of LSTM modeling, which will be useful as deep learning approaches to hydrologic modeling become more prominent and modelers look for ways to infer physical information from data-driven predictions.