Journal of Hydrology X最新文献

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.

The regionalisation of flood frequencies is a precondition for the estimation of flood statistics for ungauged basins. It is often based on either the concept of hydrological similarity of catchments or spatial proximity. Similarity is usually defined by comparing catchment attributes or distances. Here, we apply flood types in regionalisation directly to consider the type-specific aspects of similarity. The different flood types are classified according to their meteorological causes and hydrographs. Their probability distributions are modelled by type-specific distribution functions which are combined into one statistical annual mixture model afterwards. For regionalisation, we specified the parameters of each type-specific probability distribution separately with hierarchical clustering and regressions from catchment attributes. By selection of most relevant features, depending on the flood type, the specifics of flood-generating processes and meteorological causes were considered. The results demonstrate how this consideration of deterministic aspects can improve the transferability of distribution parameters to ungauged catchments. The type-specific regionalisation approach offers a higher degree of freedom for regionalisation as it describes the relationships between catchment characteristics, meteorological causes of floods and response of watersheds.

GRACE (Gravity Recovery and Climate Experiment) and GRACE-FO (Follow-On) satellites have provided unique insights into the evolution of Terrestrial Water Storage (TWS) in space and time. Despite such advancements, various GRACE solutions produced by different data centers display uneven spatial attributes with varying associated uncertainties. Via spatial diagnostics tools and a modified triple collocation (MTC) approach, this research evaluates the TWS (terrestrial water storage) trend estimations “on the grid-scale” from 11 gridded GRACE products of RL05 and RL06 releases between 2002 and 2017. Distinct from classic TCA (triple collocation analysis), the MTC employs a GWR (geographically weighted regression) scaling scheme with distinctive spatial coefficients. The spatial diagnostics analyses identified different autocorrelation patterns, clustering tendencies of hot (positive) and cold (negative) spots agglomeration at varying spatial width, and unique frequency distributions. The results indicated that within a 10-degree spatial radius the SHs (Spherical Harmonics) of RL05 and RL06 are highly autocorrelated compared to the mascons (mass concentration blocks) solutions. The spatial clustering results revealed that many solutions agreed on the overall directions and distribution of the hot and cold spots. The clustering among mascon products, however, reflected more localized mass anomalies. At the scale of drainage basins, the trend magnitude, as well as their associated uncertainties appeared to be driven by the occurrence of spatial clusters within the basin area. The MTC results showed that the uncertainty patterns follow the same spatial extent within each cluster. The MTC analysis underscored the added benefits of cluster analysis and the GWR scaling over the classic OLS approach.

Green roofs can be valuable components in sustainable urban drainage systems, and hydrological models may provide useful information about the runoff from green roofs for planning purposes. Various models have been proposed in the literature, but so far no papers have compared the performance of multiple models across multiple full-size green roofs. This paper compared 4 models: the conceptual models Urbis and SWMM and the physically-based models Hydrus-1D and Mike SHE, across two field sites (Lyon, France and Umeå, Sweden) and two calibration periods for each site. The uncertainty and accuracy of model predictions were dependent on the selected calibration site and period. Overall model predictions from the simple conceptual model Urbis were least accurate and most uncertain; predictions from SWMM and Mike SHE were jointly the best in terms of raw percentage observations covered by their flow prediction intervals, but the uncertainty in the predictions in SWMM was smaller. However, predictions from Hydrus were more accurate in terms of how well the observations conformed to probabilistic flow predictions. Mike SHE performed best in terms of total runoff volume. In Urbis, SWMM and Hydrus uncertainty in model predictions was almost completely driven by random uncertainty, while parametric uncertainty played a significant role in Mike SHE. Parameter identifiability and most likely parameter values determined with the DREAM Bayesian algorithm were found to be inconsistent across calibration periods in all models, raising questions about the generalizability of model applications. Calibration periods where rainfall retention was highly variable between events were more informative for parameter values in all models.