Journal of Hydrology X最新文献

The objective of this study was to characterise the primary forcing variables and system feedback responsible for daily waterflow dynamics within a large, international river system (Canada and USA) during 17 melt seasons from 2001 to 2018. An analysis based on extreme gradient boosting showed that daily waterflow in four subcatchments of the upper Saint John River (SJR, Wolastoq) basin during the 17 melt seasons was to a large measure controlled by the area’s seasonal warming associated with the springtime increase in regional incident global radiation and northeasterly advection of sensible and latent heat from southerly locations. Historically, seasonal surges in air temperature and cumulative snow degree-days were shown to contribute to roughly 60% of the control on subcatchment discharge by influencing the production and timing of snowmelt. Peak accumulation of snow on the ground provided the second most important control of discharge, accounting for about 15.6% of the overall control at a daily timescale. Cumulative short- and long-term forest cover losses in the four subcatchments provided some control, but at varying levels (i.e., 4.8–14.2%) dependent on the extent of total forest cover loss and other subcatchment traits. Convergent cross mapping confirmed the unidirectional, causal relationship between annual forest cover loss and daily discharge rates at the outlet of three of the four subcatchments. The strength of the annual-forest-cover-removal-to-daily-discharge signal within the four subcatchments varied, with the subcatchment with the least annual forest cover loss (<1%, over the 17 years), predictably displaying the weakest signal (p = 0.282). Forest cover removal was shown to increase springtime discharge for all subcatchments, albeit at different rates. This work provides a more comprehensive, mechanistic interpretation of daily snowmelt control of stream/river flow dynamics in northeastern North America.

This article focuses on developing a data assimilation system that combines the modeled surface moisture estimates and satellite observations. Specifically, model states simulated by the Noah-MP land surface model are updated using an Ensemble Kalman Filter with products from the NASA SMAP (Soil Moisture Active Passive) satellite mission. The land surface model is run on two different regular grids, one at 12.5 km and the other at 500 m to produce surface and root zone soil moisture estimates across Oklahoma during April-July 2015. In the first case, the model was forced with the NLDAS-2 (North America Land Data Assimilation System) dataset and in the second with a downscaled version of the same dataset. Ground observations from the Oklahoma Mesonet network are compared to surface and root zone soil moisture output simulated by three different Noah-MP model runs i) an open loop simulation (in which no satellite data are assimilated); ii) assimilation of the 36 km SMAP radiometer-only product, and iii) assimilation of the 9 km SMAP radiometer-radar combined product. Results show that SMAP soil moisture retrievals improve the model performance (i.e., with respect to the open loop run) and that forcing the land surface model with higher resolution atmospheric forcings yields higher correlations and smaller errors in soil moisture simulations with respect to the original NLDAS-2 dataset. Although root zone soil moisture is not directly assimilated (since satellite observations are limited to the top 5 cm of the soil column), the assimilation of SMAP products at the surface is transferred to lower layers by the modeled physical processes and is shown to improve root zone soil moisture estimates as well.

In the study, we analyze changes in groundwater pressure observed in several boreholes drilled in and around the Mizunami Underground Research Laboratory (MIU) induced by the 2011 off the Pacific coast of Tohoku Earthquake (Mw 9.0). The aim of this project is a development of methodology to evaluate systematic fault activity by numerical analysis. To reach this goal we investigate the behavior of the fault zones present in the area during the passing of seismic waves. We built a simplified hydrogeological model of the MIU site and performed a series of fluid flow simulations with TOUGH2 flow numerical code. We investigate how changes in permeability along three faults present in the study area: the Tsukiyoshi Fault, the Hiyoshi Fault and the Main-Shaft Fault may have influence the groundwater level monitored in boreholes intervals. We also test the influence of the cone of depression at the MIU site and the hydraulic connectivity between the sedimentary cover and the granite aquifers. Our results suggest that two main mechanisms are responsible for the observed changes in groundwater pressure: (1) crustal dilation induced by the Tohoku earthquake causing a groundwater recharge from the sedimentary aquifers to the Toki granite aquifer where the sedimentary cover is thick; and (2) permeability increase along faults critically oriented for shear reactivation and oriented in the direction of the passing seismic wave. In this case, the seismic wave increases the shear stress acting on the fault promoting slip and a change in permeability through a mechanism of slip-induced dilation. Faults not critically stressed and faults critically oriented for shear reactivation but oriented perpendicular to the passing seismic wave are not reactivated.

We appreciate the comments by Muñoz-Carpena et al. (2021), which pointed out some inconsistency between our work and their results. After a thorough examination, we identified an inaccurate description of the soil hydraulic property model applied in Wu et al. (2021) and an associated inconsistency in parameterizing the model in Section 4.1 and Figure 6. After correcting the parameters, we found that the simulated infiltration rates of Wu et al. (2021)’s model, SWINGO, and Hydrus-1D are close under both steady and unsteady rainfalls. Moreover, Wu et al. (2021)’s model has been further validated using the experimental data of Vachaud et al. (1974) and Chu (1997) and the results show satisfactory performance of Wu et al. (2021)’s model in simulating infiltration in soils with a shallow water table (WT).

Increasing river floods and infrastructure development in many parts of the world have created an urgent need for accurate high-resolution flood hazard mapping for more efficient flood risk management. Mapping accuracy hinges on the quality of the underlying Digital Terrain Model (DTM) and other spatial datasets. This article presents a processing strategy to ensure consistent adaption of countrywide spatial datasets to the requirements of hydraulic modelling. The suggested methods are automatized to a large extent and include (i) automatic fitting of river axis positions to the DTM, (ii) detection of culverts and obstacles in the river channel (iii) Smooth elimination of obstacles by interpolation along the river axes (iv) geometric detection of water-land borders and the top edge of embankments for (v) integration of the submerged river bed geometry into the DTM. The processing chain is applied to a river network (33880 km) and a DTM from Airborne Laser Scanning (ALS) with 1 m spatial resolution covering the entire territory of Austria ($\sim$84000 ${\mathrm{km}}^2$). Thus, countrywide consistency of data and methods is achieved along with high local relevance. Semi-automatic validation and extensive manual checks demonstrate that processing significantly improves the DTM with respect to topographic and hydraulic consistency. However, some open issues of automatic processing remain, e.g. in case of long underground river reaches.

In this paper, we propose a set of simple benchmarks for the evaluation of data-based models for real-time streamflow forecasting, such as those developed with sophisticated Artificial Intelligence (AI) algorithms. The benchmarks are also data-based and provide context to judge incremental improvements in the performance metrics from the more complicated approaches. The benchmarks include temporal and spatial persistence, persistence corrected for baseflow and streamflow, as well as river distance weighted runoff obtained from space-time distributed rainfall. In the development of the benchmarks, we use basic hydrologic insights such as flow aggregation by the river network, scale-dependence in basin response, streamflow partitioning into quick flow and baseflow, water travel time, and rainfall averaging by the basin width function. The study uses 140 streamflow gauges in Iowa that cover a range of basin scales between 7 and 37,000 km². The data cover 17 years. This work demonstrates that the proposed benchmarks can provide good performance according to several commonly used metrics. For example, streamflow forecasting at half of the test locations across years achieves a Kling-Gupta Efficiency (KGE) score of 0.6 or higher at one-day ahead lead time, and 20% of cases reach the KGE of 0.8 or higher. The proposed benchmarks are easy to implement and should prove useful for developers of data-based as well as physics-based hydrologic models and real-time data assimilation techniques.

This study presents an inverse modeling strategy for organic contaminant source localization. The approach infers the hydraulic conductivity field, the dispersivity, and the source zone location. Beginning with initial observed data of contaminant concentration and hydraulic head, the method follows an iterative strategy of adding new observations and revising the source location estimate. Non-linear optimization using the Gauss-Levenberg-Marquardt Algorithm (PEST++) is tested at a real contaminated site. Then a limited number of drilling locations are added, with their positions guided by the Data Worth analysis capabilities of PYEMU. The first phase of PEST++, with PYEMU guidance, followed by addition of monitoring wells provided an initial source location and identified four additional drilling locations. The second phase confirmed the source location, but the estimated hydraulic conductivity field and the Darcy flux were too far from the measured values. The mismatch led to a revised conceptual site model that included two distinct zones, each with a plume emanating from a separate source. A third inverse modelling phase was conducted with the revised site conceptual model. Finally, the source location was compared to results from a Geoprobe@ MiHPT campaign and historical records, confirming both source locations. By merging measurement and modeling in a coupled, iterative framework, two contaminant sources were located through only two drilling campaigns while also reforming the conceptual model of the site.

In this comment we draw attention to parametrization errors in this recently published article when comparing an existing model for soil infiltration under shallow water conditions, SWINGO, with an alternative solution and Richards benchmark solution. After correcting the errors, a new model comparison shows SWINGO ability to match the other approaches and supports the general validity of SWINGO’s simplified approach against the more complicated solutions.

The recent increase in the frequency of urban flooding in Bangkok has led to speculation that global warming may be to blame. Assessing this, however, is challenging, as Bangkok represents an ever-changing environment with changing storm drainage infrastructure, limited flood and precipitation data, and a tropical setting that complicates the relationship precipitation extremes exhibit with temperature. This study attempts to create a framework to investigate the merits of the above speculation, using ground observations of precipitation maxima, flood inundation, and dew point temperature, along with simulations from General Circulation Models (GCMs) to present multiple lines of evidence to compensate for the weaknesses any individual evidence may have. The complexity of flooding in an urban stormwater drainage network is accounted by focussing instead on flood inundation information conditional to the incident dew point temperature which is increasing as a result of warming. The assessment identifies a markedly different pattern of change in the east versus the west of the city, attributing this to population change in the two parts, further complicating the link to global warming. Application of the developed methodology using the most recent GCM simulations available suggests the increase in flooding is a pattern that can be expected to continue.

Growing agricultural water demand is dramatically affecting the implementation of, and compliance with, water sharing plans in regions such as the Murray-Darling Basin (MDB). Problems can arise from water theft, poor resourcing or questionable actions from stakeholders. Recent actions from MDB governments have resulted in improved regulation, although more is required in a technical, governance and cultural space to create a comprehensive and transparent management framework. This is pivotal in improving overall trust in water regulators. We discuss an integrated water resource management approach for improved water regulation, involving the implementation of remote sensing technologies to complement metering, coupled with a focus on a stronger compliance culture in a range of stakeholder groups and regulatory changes that allow quicker adoption of unbiased best practice science and technology.