Hydrological Problems Research Articles

Improving Radar-Based Rainfall Forecasts by Long Short-Term Memory Network in Urban Basins

Radar-based rainfall forecasts are widely used extrapolation algorithms that are popular in systems of precipitation for predicting up to six hours in lead time. Nevertheless, the reliability of rainfall forecasts gradually declines for heavy rain events with lead time due to the lack of predictability. Recently, data-driven approaches were commonly implemented in hydrological problems. In this research, the data-driven models were developed based on the data obtained from a radar forecasting system named McGill Algorithm for Precipitation nowcasting by Lagrangian Extrapolation (MAPLE) and ground rain gauges. The data included thirteen urban stations in the five metropolitan cities located in South Korea. The twenty-five data points of MAPLE surrounding each rain station were utilized as the model input, and the observed rainfall at the corresponding gauges were used as the model output. The results showed superior capabilities of long short-term memory (LSTM) network in improving 180-min rainfall forecasts at the stations based on a comparison of five different data-driven models, including multiple linear regression (MLR), multivariate adaptive regression splines (MARS), multi-layer perceptron (MLP), basic recurrent neural network (RNN), and LSTM. Although the model still produced an underestimation of extreme rainfall values at some examined stations, this study proved that the LSTM could provide reliable performance. This model can be an optional method for improving rainfall forecasts at the stations for urban basins.

A Robust Gaussian variogram estimator for cartography of hydrological extreme events

Kriging is used for spatial interpolation of geophysical and hydrological variables. This technique requires the definition of a mathematical function of spatial correlation known as variogram. Extreme value mapping is critical for the analysis of hydrological problems such as multiple risk and disaster damage valuation. However, the formulation of a variogram is complex and the selection of the variogram model is often crucial in the final representation of extreme values. There is some evidence that a variogram estimator may reduce the negative impact of using data samples with outliers. This is an approach that eliminates the term that takes into consideration the squared differences of empirical values in the calculation of a variogram. Known as Cressie–Hawkins Estimator (CH), it is based on considering a Gaussian distribution for the calculation of the variogram. The CH satisfies the main statistical considerations of normality; however, the formulation of kurtosis is not used to guarantee total normality. The present paper modifies the CH, adding the term corresponding to the fourth statistical moment. The numerical example proposed in the literature to evaluate the efficiency of the CH is reproduced, and it is verified that the new variogram (CH-GLo) decreases further the effect of the extreme values in the cartography. CH-GLo is applied to cartography an extreme storm that happened in August 2014 in the city of Queretaro, Mexico. It is concluded that this new variogram formulation is a suitable alternative for mapping extreme values monitored in real-time in urban areas.

Identification of drought level using normalized difference latent heat index on Maros Watershed

One of the impacts of climate change is drought. Drought is a hydrological problem that significantly affects survival on earth. This study aims to identify drought through the approach of one of the indices related to water, namely Normalized Difference Latent Heat (NDLI). NDLI values were obtained through a multispectral Landsat OLI calculation process, namely band 3 - green, band 4 - red, and band 6 – SWIR. Each band interprets variables related to heat and water content. The coverage area of research is in the Maros watershed, which is one of the watershed supplying water to the city of Makassar. Maros watershed has an area of ± 72,000 ha with relatively high human activity. NDLI values range from +1 to -1, where positive values indicate areas with good water content with latent heat and poor water content. In Maros watershed, NDLI values ranged from 0.37 to -0.25. Based Drought classification level, the level of drought in Maros watershed is moderately wet to moderately drought, and mildly wet is the most extensive drought levels in Maros watershed.

Rainfall runoff modeling for the basin in Bukit Kledang, Perak

A fundamental problem in studying a hydrological system is that most of the reaction takes place underground. Precipitation Runoff demonstrating of the Johan River basin was carried out using HEC-HMS technique. The model can improve decision-making about the hydrological problem, whether that be in water resources planning, flood protection and mitigation of contamination. The result shows that the rainfall-runoff model for the Bukit Kledang basin is slow depending on the relative travel time of basin and also associated with soil type, land used, multiple storms and seasonality.

Hydrological regime and runoff formation of the Chulym river and its analysis

One of the main problems of hydrology is the formation of rainfall floods and the assessment of the hydrological regime of rivers. Analysis of the factors of river runoff formation helps to calculate the main hydrological characteristics even in the river sections not illuminated by field data. One of the regional methods for calculating hydrological characteristics is the calculation of the dependences of runoff characteristics on the physical and geographical factors of the catchment area. Such calculations are based on the results of generalizing the data of hydrometeorological observations in the design area. There is often a need for runoff characteristics in the design section of the river where there are no field data of hydrometric observations, and this problem is considered in the proposed study. This is the first time this approach has been implemented for the Chulym River. This article examines the analysis of the factors of the river runoff of the Chulym river, which makes it possible to make objective calculations of the main hydrological characteristics in the sections of the rivers not illuminated by field observations.

Efficient multiscale imaging of subsurface resistivity with uncertainty quantification using ensemble Kalman inversion

SUMMARYElectrical resistivity tomography (ERT) is widely used to image the Earth’s subsurface and has proven to be an extremely useful tool in application to hydrological problems. Conventional smoothness-constrained inversion of ERT data is efficient and robust, and consequently very popular. However, it does not resolve well sharp interfaces of a resistivity field and tends to reduce and smooth resistivity variations. These issues can be problematic in a range of hydrological or near-surface studies, for example mapping regolith-bedrock interfaces. While fully Bayesian approaches, such as those using Markov chain Monte Carlo sampling, can address the above issues, their very high computation cost makes them impractical for many applications. Ensemble Kalman inversion (EKI) offers a computationally efficient alternative by approximating the Bayesian posterior distribution in a derivative-free manner, which means only a relatively small number of ‘black-box’ model runs are required. Although common limitations for ensemble Kalman filter-type methods apply to EKI, it is both efficient and generally captures uncertainty patterns correctly. We propose the use of a new EKI-based framework for ERT which estimates a resistivity model and its uncertainty at a modest computational cost. Our EKI framework uses a level-set parametrization of the unknown resistivity to allow efficient estimation of discontinuous resistivity fields. Instead of estimating level-set parameters directly, we introduce a second step to characterize the spatial variability of the resistivity field and infer length scale hyperparameters directly. We demonstrate these features by applying the method to a series of synthetic and field examples. We also benchmark our results by comparing them to those obtained from standard smoothness-constrained inversion. Resultant resistivity images from EKI successfully capture arbitrarily shaped interfaces between resistivity zones and the inverted resistivities are close to the true values in synthetic cases. We highlight its readiness and applicability to similar problems in geophysics.

Stochastic hydrology: the development of main ideas in Russia

The aim of the work is to assess the current state of scientific research and the main results in the field of stochastic hydrology. Various problems of scientific and applied hydrology are solved on the basis of different types of stochastic models. On the basis of these models, methods are being developed for obtaining secure engineering runoff characteristics, or reliable hydrological forecasts of various lead times. Most of the water management, construction, environmental and other problems are solved today within the framework of probabilistic models and statistical methods. The main tasks of stochastic hydrology can be divided into several groups. Despite the discussion in hydrology on the applicability of the theory of probability and random processes to the analysis of fluctuations of hydrometeorological series, the problem of choosing a research methodology remains one of the most important, and is often discussed in connection with anomalous manifestations of hydrological phenomena (extreme floods, prolonged droughts). An important applied and theoretical problem in hydrology is the choice of a one-dimensional probability distribution. In this part, discussions are important both on the problem of the type of new distributions and on methods for estimating parameters. Ideas and hypotheses are considered that can significantly increase the reliability of statistical characteristics. Climatic changes in many cases cause disturbances in the stationarity of time series of hydrological characteristics. Progress in the development of new approaches to probabilistic modeling is associated both with the well-known problem of the sufficiency of the Markov hypothesis and with the need to introduce new, more complex models and Bayesian estimation methods. The article is devoted to the discussion of the current state of these problems in the form of a scientific review.

Геоморфометрия сегодня

Topography is the most important component of the geographical shell, one of the main elements of geosystems, and the framework of a landscape. geomorphometry is a science, the subject of which is modeling and analyzing the topography and the relationships between topography and other components of geosystems. Currently, the apparatus of geomorphometry is widely used to solve various multi-scale problems of the Earth sciences. As part of the RFBR competition “Expansion”, we present an analytical review of the development of theory, methods, and applications of geomorphometry for the period of 2016–2021. For the analysis, we used a sample of 485 of the strongest and most original papers published in international journals belonging to the JCR Web of Science Core Collection quartile I and II (Q1–Q2), as well as monographs from leading international publishers. We analyze factors caused a progress in geomorphometry in recent years. These include widespread use of unmanned aerial survey and digital photogrammetry, development of tools and methods for survey of submarine topography, emergence of new publicly available digital elevation models (DEMs), development of new methods of DEM preprocessing for their filtering and noise suppression, development of methods of two-dimensional and three-dimensional visualization of DEMs, introduction of machine learning techniques, etc. We consider some aspects of the geomorphometric theory developed in 2016–2021. In particular, a new classification of morphometric values is presented. We discuss new computational methods for calculating morphometric models from DEM, as well as the problems facing the developers and users of such methods. We consider application of geomorphometry for solving multiscale problems of geomorphology, hydrology, soil science, geology, glaciology, speleology, plant science and forestry, zoogeography, oceanology, planetology, landslide studies, remote sensing, urban studies, and archaeology.

Bayesian Regularized Neural Network Model Development for Predicting Daily Rainfall from Sea Level Pressure Data: Investigation on Solving Complex Hydrology Problem

Prediction of daily rainfall is important for flood forecasting, reservoir operation, and many other hydrological applications. The artificial intelligence (AI) algorithm is generally used for stochastic forecasting rainfall which is not capable to simulate unseen extreme rainfall events which become common due to climate change. A new model is developed in this study for prediction of daily rainfall for different lead times based on sea level pressure (SLP) which is physically related to rainfall on land and thus able to predict unseen rainfall events. Daily rainfall of east coast of Peninsular Malaysia (PM) was predicted using SLP data over the climate domain. Five advanced AI algorithms such as extreme learning machine (ELM), Bayesian regularized neural networks (BRNNs), Bayesian additive regression trees (BART), extreme gradient boosting (xgBoost), and hybrid neural fuzzy inference system (HNFIS) were used considering the complex relationship of rainfall with sea level pressure. Principle components of SLP domain correlated with daily rainfall were used as predictors. The results revealed that the efficacy of AI models is predicting daily rainfall one day before. The relative performance of the models revealed the higher performance of BRNN with normalized root mean square error (NRMSE) of 0.678 compared with HNFIS (NRMSE = 0.708), BART (NRMSE = 0.784), xgBoost (NRMSE = 0.803), and ELM (NRMSE = 0.915). Visual inspection of predicted rainfall during model validation using density‐scatter plot and other novel ways of visual comparison revealed the ability of BRNN to predict daily rainfall one day before reliably.

Deep neural networks in hydrology: the new generation of universal and efficient models

For around a decade, deep learning – the sub-field of machine learning that refers to artificial neural networks comprised of many computational layers – modifies the landscape of statistical model development in many research areas, such as image classification, machine translation, and speech recognition. Geoscientific disciplines in general and the field of hydrology in particular, also do not stand aside from this movement. Recently, the proliferation of modern deep learning-based techniques and methods has been actively gaining popularity for solving a wide range of hydrological problems: modeling and forecasting of river runoff, hydrological model parameters regionalization, assessment of available water resources, identification of the main drivers of the recent change in water balance components. This growing popularity of deep neural networks is primarily due to their high universality and efficiency. The presented qualities, together with the rapidly growing amount of accumulated environmental information, as well as increasing availability of computing facilities and resources, allow us to speak about deep neural networks as a new generation of mathematical models designed to, if not to replace existing solutions, but significantly enrich the field of geophysical processes modeling. This paper provides a brief overview of the current state of the field of development and application of deep neural networks in hydrology. Also in the following study, the qualitative long-term forecast regarding the development of deep learning technology for managing the corresponding hydrological modeling challenges is provided based on the use of “Gartner Hype Curve”, which in the general details describes a life cycle of modern technologies.

State-of-the-art potential of the GRACE satellite mission for solving modern hydrological problems

The paper presents main results of GRACE mission using in such fields of study as estimations of components of basins water storage and water balance, hydrological modeling. It is shown that error of GRACE data is of the order 11 mm for watersheds with area about 100 000 km2 and decreasing with increasing of basin area. This accuracy made it possible to identify long-term and seasonal water storage. It is shown, that decreasing of total water storage in the Don basin for 2002–2019 is approximately equally caused by both soil moisture and groundwater changes. At the same time, minimum of groundwater was already reached in 2010, and soil moisture in 2015. Since 2016, Don basin groundwater changes a little during the winter period that is due, probably, with increase number of thaws and thinning of the freezing layer during this period. By the data of meteorological stations for the precipitation of cold period for the European Russia the value of their systematic error was estimated, it is about 20-25%. The comparison of the values of total water storage for the river basins of the north part of European part of Russia, according GRACE data and ECOMAG runoff modeling results has shown their good coincidence (NSE =0.78 0.89). In comparison with GRACE, ECOMAG shows a smaller increase in water storage during the winter and a faster decline during spring flood period. Currently, progress in the use of GRACE in hydrology is limited by low spatial-temporal resolution of data, which, within the framework of the GRACE mission itself, will not be improved in the coming years. At the same time, the principle of GRACE operation can be applied in future to various satellite constellations.

A New Runoff Routing Scheme for Xin’anjiang Model and Its Routing Parameters Estimation Based on Geographical Information

The Xin’anjiang model is a conceptual hydrological model, which has an essential application in humid and semi-humid regions. In the model, the parameters estimation of runoff routing has always been a significant problem in hydrology. The quantitative relationship between parameters of the lag-and-route method and catchment characteristics has not been well studied. In addition, channels in Muskingum method of the Xin’anjiang model are assumed to be virtual channels. Therefore, its parameters need to be estimated by observed flow data. In this paper, a new routing scheme for the Xin’anjiang model is proposed, adopting isochrones method for overland flow and the grid-to-grid Muskingum–Cunge–Todini (MCT) method for channel routing, so that the routing parameters can be estimated according to the geographic information. For the new routing scheme the average overland flow velocity can be determined through the land cover and overland slope, and the channel routing parameters can be determined through channel geometric characteristic, stream order and channel gradient. The improved model was applied at a 90 m grid scale to a nested watershed located in Anhui province, China. The parent Tunxi watershed, with a drainage area of 2692 km2, contains four internal points with available observed streamflow data, allowing us to evaluate the model’s ability to simulate the hydrologic processes within the watershed. Calibration and verification of the improved model were carried out for hourly time scales using hourly streamflow data from 1982 to 2005. Model performance was assessed by comparing simulated and observed flows at the watershed outlet and interior gauging stations. The performance of both original and new runoff routing schemes were tested and compared at hourly scale. Similar and satisfactory performances were achieved at the outlet both in the new runoff routing scheme using the estimated routing parameters and in the original runoff routing scheme using the calibrated routing parameters, with averaged Nash-Sutcliffe efficiency (NSE) of 0.92 and 0.93, respectively. Moreover, the new runoff routing scheme is also able to reproduce promising hydrographs at internal gauges in study catchment with the mean NSE ranging from 0.84 to 0.88. These results indicate that the parameter estimation approach is efficient and the developed model can satisfactorily simulate not only the streamflow at the parent watershed outlet, but also the flood hydrograph at the interior gauging points without model recalibration. This study can provide some guidance for the application of the Xin’anjiang model in ungauged areas.

The origin of hysteresis and memory of two-phase flow in disordered media

Cyclic fluid-fluid displacements in disordered media feature hysteresis, multivaluedness, and memory properties in the pressure-saturation relationship. Quantitative understanding of the underlying pore-scale mechanisms and their extrapolation across scales constitutes a major challenge. Here we find that the capillary action of a single constriction in the fluid passage contains the key features of hysteresis. This insight forms the building block for an ab initio model that provides the quantitative link between the microscopic capillary physics, spatially-extended collective events (Haines jumps) and large-scale hysteresis. The mechanisms identified here apply to a broad range of problems in hydrology, geophysics and engineering.

Edmond Halley's Contributions to Hydrogeology.

Edmond Halley's Contributions to Hydrogeology.

Hydroinformatics and the web

Climate change and global sustainability are among the most urgent concerns facing humanity. One important aspect of this is hydrology, i.e., the scientific study of the movement, distribution and management of water. In light of the pervasiveness of Web technology and more broadly of information and communication technology (ICT), this article presents an overview of recent advances in Web data analytics for hydrology and hydroinformatics, via scientific perspectives, experiments, and tools pertaining to hydrology data and its relevance to areas such as climate change. Researchers have conducted studies on hydrological problems that are spreading globally, and the role of the Web is crucial here for providing ubiquitous access to information, enabling its efficient analysis and adequately disseminating the results via websites and apps. This aids in predictive analytics and decision support. We address the management, mining, and dissemination of hydrology data with the long-term goal of global sustainability. We survey classical and state-of-the-art works in hydroinformatics and the Web, also including recent COVID-19 related issues, and discuss notable open avenues for future research both with regard to climate change as well as the future of the Web.

Hydrological problems of flash floods and the encroachment of wastewater affecting the urban areas in Greater Cairo, Egypt, using remote sensing and GIS techniques

BackgroundThis paper discusses the hydrological problems assessment of flash floods and the encroachment of wastewater in selected urban areas of Greater Cairo using remote sensing and geographic information system (GIS) techniques. The integration of hydrogeological and geomorphological analyses with the fieldwork of drainage basins (Wadi Degla) hosting these urban areas endeavors to provide the optimum mitigation measures that can be feasibly taken to achieve sustainability of the urban areas and water resources available.ResultsLandsat 5 and Sentinel-2 satellite images were obtained shortly before and after flash flood events and were downloaded and analyzed to define the active channels, urban interference, storage areas, and the natural depressions response. The quantitative flash flood estimates include total GSMap meteorological data sets, parameters of rainfall depths from remote sensing data, active channel area from satellite images, and storage areas that flooded. In GIS, digital elevation model was used to estimate the hydrographic parameters: flow direction within the catchment, flow accumulation, time zone of the catchment, and estimating of the water volume in the largely inundated depressions.ConclusionsBased on the results obtained from the study of available satellite images, it has been shown that there are two significant hydrological problems, including the lack of flash flood mitigation measures for urban areas, as the wastewater depressions and sanitary facilities are dotting in the downstream areas.

Developing a Reliability Index of Low Impact Development for Urban Areas

A defining characteristic of the urbanization is the transformation of existing pervious areas into impervious areas during development. This leads to numerous hydrologic and environmental problems such as an increase in surface runoff due to excess rainfall, flooding, the deterioration of water quality, and an increase in nonpoint source pollution. Several studies propose supplementary measures on environmental change problems in development areas using the low impact development technique. This study investigated the reduction of nonpoint source pollutant loads and flooding in catchments through urban catchment rainfall–runoff management. For the quantitative assessment of flood disasters and water pollution problems, we propose a reliability evaluation technique. This technique refers to a series of analysis methods that determine the disaster prevention performance of the existing systems. As the two factors involve physical quantities of different dimensions, a reliability evaluation technique was developed using the distance measure method. Using the storm water management model, multiple scenarios based on synthetic rainfall in the catchment of the Daerim 2 rainwater pumping station in Seoul, South Korea, were examined. Our results indicate the need for efficient management of natural disaster risks that may occur in urban catchments. Moreover, this study can be used as a primary reference for setting a significant reduction target and facilitating accurate decision making concerning urban drainage system management.

Explicit the urban waterlogging spatial variation and its driving factors: The stepwise cluster analysis model and hierarchical partitioning analysis approach

Urban waterlogging is a hydrological cycle problem that seriously affects people's life and property. Characterizing waterlogging variation and explicit its driving factors are conducive to prevent the damage of such disasters. Conventional methods, because of the high spatial heterogeneity and the non-stationary complex mechanism of urban waterlogging, are not able to fully capture the urban waterlogging spatial variation and identify the waterlogging susceptibility areas. A more robust method is recommended to quantify the variation trend of urban waterlogging. Previous studies have simulated the waterlogging variation in relatively small areas. However, the relationship between variables is often ignored, which cannot comprehensively reveal the dominant drivers affecting urban waterlogging. Therefore, a novel approach is proposed that combined stepwise cluster analysis model (SCAM) and hierarchical partitioning analysis (HPA) within a general framework and verifies the applicability through logistic regression, artificial neural network, and support vector machine. According to the dominant driving factors, different simulation scenarios are established to analyze waterlogging density variation. Results found that the SCAM provides accurate and detailed simulated results both in urban centers where waterlogging frequently occurs and urban fringe with few waterlogging events, which shows an excellent performance with a high classification accuracy and generalization capability. HPA detected that the impervious surface abundance (28.07%), vegetation abundance (20.80%), and cumulate precipitation (16.25%) are the dominant drivers of waterlogging. This result suggests that priority should be given to controlling these three factors to mitigate the risk of waterlogging. It is interesting to note that under different urbanization and rainfall scenarios, the urban waterlogging susceptibility has a considerable variation. The watershed spatial location and watershed characteristics are relevant aspects to be considered in identifying and assessing waterlogging susceptibility, which provides original insights that urban waterlogging mitigation strategies should be developed according to different local conditions and future scenarios.

El Frankenstein Urbano: Ecólogos, Urbanistas e Ingenieros Frente a la Crisis Hidrológica de la Ciudad de México a Mitad del Siglo XX

Impactada por la industrialización de la economía nacional, a mitad del siglo XX la ciudad de México enfrentaba una explosión demográfica que extrapoló los severos problemas hidrológicos que arrastraba de décadas atrás, principalmente el hundimiento de su suelo, inundaciones, tolvaneras, y escasez de agua. Esta crisis demográfica e hidrológica generó criticascríticas y temor social por el futuro de la capital y promovió diagnósticos y proyectos de solución entre científicos de la época – principalmente ecólogos, urbanistas e ingenieros –, cuyo análisis, objeto de este artículo, muestra que para algunos científicos la ciudad y sus problemas teníanuvieron alternativas de solución opuestas al paradigma político científico dominante y, por tanto, a la decisión de prolongar el modelo urbano industrial que guiaba sus transformaciones a través de las decisiones de política pública aplicadas por el gobierno federal.

Application of Convolutional Neural Network for Spatiotemporal Bias Correction of Daily Satellite-Based Precipitation

Spatiotemporal precipitation data is one of the essential components in modeling hydrological problems. Although the estimation of these data has achieved remarkable accuracy owning to the recent advances in remote-sensing technology, gaps remain between satellite-based precipitation and observed data due to the dependence of precipitation on the spatiotemporal distribution and the specific characteristics of the area. This paper presents an efficient approach based on a combination of the convolutional neural network and the autoencoder architecture, called the convolutional autoencoder (ConvAE) neural network, to correct the pixel-by-pixel bias for satellite-based products. The two daily gridded precipitation datasets with a spatial resolution of 0.25° employed are Asian Precipitation-Highly Resolved Observational Data Integration towards Evaluation (APHRODITE) as the observed data and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR) as the satellite-based data. Furthermore, the Mekong River basin was selected as a case study, because it is one of the largest river basins, spanning six countries, most of which are developing countries. In addition to the ConvAE model, another bias correction method based on the standard deviation method was also introduced. The performance of the bias correction methods was evaluated in terms of the probability distribution, temporal correlation, and spatial correlation of precipitation. Compared with the standard deviation method, the ConvAE model demonstrated superior and stable performance in most comparisons conducted. Additionally, the ConvAE model also exhibited impressive performance in capturing extreme rainfall events, distribution trends, and described spatial relationships between adjacent grid cells well. The findings of this study highlight the potential of the ConvAE model to resolve the precipitation bias correction problem. Thus, the ConvAE model could be applied to other satellite-based products, higher-resolution precipitation data, or other issues related to gridded data.