Hydrological Research Research Articles

Why do we have so many different hydrological models? A review based on the case of Switzerland

AbstractHydrology plays a central role in applied and fundamental environmental sciences, but it is well known to suffer from an overwhelming diversity of models, particularly to simulate streamflow. We discuss here in detail how such diversity did arise based on the example of Switzerland. The case study's relevance stems from the fact that Switzerland, despite being a small country, shows a variety of hydro‐climatological regimes, of water resources management challenges, and of hydrological research institutes that led to a model diversification that stands exemplary for the diversification that arose also at larger scales. Our analysis, based on literature review, personal inquiry, and an author survey, summarizes the main driving forces behind model diversification. We anticipate that this review not only helps researchers from other fields but in particular also the international hydrology community to understand why we have so many different streamflow models.This article is categorized under: Science of Water > Science of Water Science of Water > Hydrological Processes Science of Water > Methods

Streamflow variability in the past four centuries for the largest tributary of the Yellow River and its teleconnection with large‐scale climate forcing

AbstractUnderstanding long‐term hydroclimate change and its evolution mechanism is beneficial to hydrological assessments and future planning in the Weihe River Basin, a region affected by the East Asian monsoon, as well as addressing increasing concerns about water resources due to social and economic development. The average June–July streamflow reconstruction was performed for the period from 1592 to 1988 for the Weihe River, based on the common growth responses relating tree‐ring width, minimum density and hydrological variables, as inferred from the first principal component series of tree‐ring width and density. The statistical results of calibration and verification confirmed that our model was credible and could explain 47.2% of the variance in the calibration period from 1937 to 1970. According to the criteria of one standard deviation less or greater than the mean value, 59 extremely low flow years and 62 high flow years were identified, accounting for 14.86% and 15.62%, respectively, of the past 397‐year hydrological reconstruction. Severe dryness and wetness periods were in good agreement with other hydrological records in the basin, suggesting the coherence of hydroclimate changes at the basin scale, including extreme drought events from the 1920s to 1930s. The reconstructed streamflow had significant interannual and interdecadal periodic signals associated with spatial correlations with sea surface temperatures in the Pacific, Indian and Atlantic Oceans, indicating the relationships between hydrological variations in the Weihe River and the natural forcing related to the El Niño‐Southern Oscillation, Pacific Decadal Oscillation, Indian Ocean Dipole pattern, North Atlantic Oscillation and Atlantic Multidecadal Oscillation (AMO). Our results provide new evidence for the potential of hydrological research using multiple tree‐ring indicators, such as tree‐ring width and minimum density, in the semiarid and semihumid areas of central China.

Application of Machine Learning Techniques in Rainfall–Runoff Modelling of the Soan River Basin, Pakistan

Rainfall–runoff modelling has been at the essence of research in hydrology for a long time. Every modern technique found its way to uncover the dynamics of rainfall–runoff relation for different basins of the world. Different techniques of machine learning have been extensively applied to understand this hydrological phenomenon. However, the literature is still scarce in cases of extensive research work on the comparison of streamline machine learning (ML) techniques and impact of wavelet pre-processing on their performance. Therefore, this study compares the performance of single decision tree (SDT), tree boost (TB), decision tree forest (DTF), multilayer perceptron (MLP), and gene expression programming (GEP) in rainfall–runoff modelling of the Soan River basin, Pakistan. Additionally, the impact of wavelet pre-processing through maximal overlap discrete wavelet transformation (MODWT) on the model performance has been assessed. Through a comprehensive comparative analysis of 110 model settings, we concluded that the MODWT-based DTF model has yielded higher Nash–Sutcliffe efficiency (NSE) of 0.90 at lag order (Lo4). The coefficient of determination for the model was also highest among all the models while least root mean square error (RMSE) value of 23.79 m3/s was also produced by MODWT-DTF at Lo4. The study also draws inter-technique comparison of the model performance as well as intra-technique differentiation of modelling accuracy.

A Multigrid Dynamic Bidirectional Coupled Surface Flow Routing Model for Flood Simulation

Surface flow routing is an important component in hydrologic and hydrodynamic research. Based on a literature review and comparing the different coupling models (the hydrologic model and hydrodynamic model), a multigrid dynamic bidirectional coupled surface flow routing model (M-DBCM), consisting of diffusion wave equations (DWEs) and shallow water equations (SWEs), is proposed herein based on grids with different resolutions. DWEs were applied to obtain runoff routing in coarse grid regions to improve the computational efficiency, while the DWEs and SWEs were bidirectionally coupled to detail the flood dynamics in fine grid regions to obtain good accuracy. In fine grid zones, the DWEs and SWEs were connected by an internal moving boundary, which ensured the conservation of mass and momentum through the internal moving boundary. The DWEs and SWEs were solved by using the time explicit scheme, and different time steps were adopted in regions with different grid sizes. The proposed M-DBCM was validated via three cases, and the results showed that the M-DBCM can effectively simulate the process of surface flow routing, which had reliable computational efficiency while maintaining satisfactory simulation accuracy. The rainfall runoff in the Goodwin Creek Watershed was simulated based on the proposed M-DBCM. The results showed that the discharge hydrographs simulated by the M-DBCM were closer to the measured data, and the simulation results were more realistic and reliable, which will be useful in assisting flood mitigation and management.

CCAM: China Catchment Attributes and Meteorology dataset

Abstract. The absence of a compiled large-scale catchment characteristics dataset is a key obstacle limiting the development of large-sample hydrology research in China. We introduce the first large-scale catchment attribute dataset in China. We compiled diverse data sources, including soil, land cover, climate, topography, and geology, to develop the dataset. The dataset also includes catchment-scale 31-year meteorological time series from 1990 to 2020 for each basin. Potential evapotranspiration time series based on Penman's equation are derived for each basin. The 4911 catchments included in the dataset cover all of China. We introduced several new indicators that describe the catchment geography and the underlying surface differently from previously proposed datasets. The resulting dataset has a total of 125 catchment attributes and includes a separate HydroMLYR (hydrology dataset for machine learning in the Yellow River Basin) dataset containing standardized weekly averaged streamflow for 102 basins in the Yellow River Basin. The standardized streamflow data should be able to support machine learning hydrology research in the Yellow River Basin. The dataset is freely available at https://doi.org/10.5281/zenodo.5729444 (Zhen et al., 2021). In addition, the accompanying code used to generate the dataset is freely available at https://github.com/haozhen315/CCAM-China-Catchment-Attributes-and-Meteorology-dataset (last access: 26 November 2021) and supports the generation of catchment characteristics for any custom basin boundaries. Compiled data for the 4911 basins covering all of China and the open-source code should be able to support the study of any selected basins rather than being limited to only a few basins.

A GWR downscaling method to reconstruct high-resolution precipitation dataset based on GSMaP-Gauge data: A case study in the Qilian Mountains, Northwest China

Accurate precipitation data are crucial for hydrological, meteorological, and ecological research. However, it is difficult to obtain high-precision and high-resolution spatiotemporal distributions of precipitation in remote mountain regions with complex topography and sparse rain gauges. In addition, the spatial resolutions of existing satellite precipitation products are too coarse to apply them in the mountain regions with great spatiotemporal heterogeneity. To overcome the disadvantage, downscaling satellite precipitation products has been an effectively method to develop high-resolution precipitation data. In this study, a geographically weighted regression (GWR) model, coupling with topographical and water vapor source variables filtered by stepwise regression analysis (SRA), is applied to downscale the GSMaP-Gauge precipitation products (0.1° × 0.1°) to obtain high-resolution (1 km × 1 km) precipitation from 2000 to 2020 at annual, seasonal, and monthly scales over the Qilian Mountains. Besides, the accuracy of the downscaled precipitation based on all meteorological stations and the stations at high altitude (i.e., over 3000 m) are validated. Furtherly, the spatiotemporal variations of precipitation are analyzed. The results show that: (1) the accuracy after downscaling has been improved comparing with that of the original data. The accuracy of precipitation simulated at high-altitude stations is lower than that at all stations; (2) The trend of precipitation before and after downscaling is consistent in space. The spatial distributions of precipitation at annual, spring, summer, autumn, and months from March to November are decreased from the southeast to the northwest; (3) The spatial variations of precipitation show an increasing trend in most areas (>50%) at different time scales, except for March and September. Along with the time, the annual precipitation shows an increasing trend with a slope of 3.83 over the last 20 years. These findings suggest that the GWR method can be applied effectively to downscale annual, seasonal, and monthly precipitation of GSMaP-Gauge products in the Qilian Mountains.

AN OVERVIEW OF AGENT-BASED MODELLING APPROACHES FOR INTEGRATED FLOOD MANAGEMENT

Previously, Integrated Flood Management (IFM) system has been implemented by several hydrological researchers in order to minimize the global flood risk by providing a convincing flood risk assessment and management, as well as sustainable adaptation and disaster alleviation policy. Flood risk is dynamic interaction between natural disasters and human vulnerability. Basically, methods for quantifying flood risk are fully-fledged but tend to treat artificial and economic vulnerabilities as static or subject to changes in external trends. However, interpretive research is rarely conducted to investigate people’s decision-making and acknowledge to flood warnings during flood event. The integration of Agent-Based Model (ABM) in simulating the interactions and dynamic responses of individual or organizations in a spatial environment during the flood events or prior to the events were reviewed. The ABM model is defined as a computational method used to simulate the behaviour and the interaction of autonomous decision-making entities in a network or system it is used to evaluate their impact on the entire system. Therefore, the ABM approach has been chosen to emulate the complexity of the IFM process due to its capability and flexibility to simulate the dynamic of human-environment scenarios in the spatial environment.

Comprehensive Evaluation of Global Precipitation Measurement Mission (GPM) IMERG Precipitation Products over Mainland China

Due to the difficulty involved in obtaining and processing a large amount of data, the spatial distribution of the quality and error structure of satellite precipitation products and the climatic dependence of the error sources have not been studied sufficiently. Eight statistical and detection indicators were used to compare and evaluate the accuracy of the Integrated Multi-Satellite Retrievals for Global Precipitation Measurement Mission (GPM IMERG) precipitation products in China, including IMERG Early, Late, and Final Run. (1) Based on the correlation coefficient between GPM IMERG precipitation products and measured precipitation, the precipitation detection ability is good in eastern China, whereas the root-mean-square error increases from northwest to southeast. (2) Compared with the Early and Late Run, the accuracy of the detection of a light rain of the IMERG Final Run is higher, but the precipitation is overestimated. With the increase in the precipitation intensity, the detection ability weakens, and the precipitation is underestimated. (3) The Final Run has a higher estimation accuracy regarding light rain in western high-altitude areas, whereas the accuracy of the detection of moderate rain, heavy rain, and rainstorms is higher in eastern coastal low-altitude areas. This phenomenon is related to the performance and detection principles of satellites. The altitude and magnitude of the precipitation affect the detection accuracy of the satellite. This study provides guidance for the application of GPM IMERG precipitation products in hydrological research and water resource management in China.

Quantifying wind-induced undercatch in the precipitation measurements at Ukrainian weather stations

Literature overview. Precipitation measurements include random and systematic errors. Systematic errors increase in the following order: evaporation loss, wetting loss, and wind-induced undercatch (World Meteorological Organization, 2008). The last one occurs because of the aerodynamic blockage under the precipitation gauge collector (Baghapour et al. 2017; Sevruk & Nespor, 1994). Field experiments have shown that wind-induced undercatch reaches 14% for rain and 40% for snow for the Tretyakov wind-shielded gauge (Goodison et al., 1998). In Ukraine, precipitation records omit wind-induced undercatch correction. This study aims to calculate true precipitation values at Ukrainian weather stations, evaluate existing methodologies for precipitation measurements correction, and create the digital archive of corrected precipitation values based on sub-daily observations. Material and methods. We used four methods to quantify wind-related errors for the Tretyakov gauge with wind shield proposed by Golubev (Konovalov et al., 2000), Bryazgin (Aleksandrov et al., 2005), Norway meteorological institute (Forland et al., 1996), and Yang (Yang et al., 1995). Sub-daily records were requested from Central Geophysical Observatory named after Boris Sreznevsky covering 207 stations between 1976 and 2019; 187 stations had more than 20 years’ period. Results. For the Tretyakov gauge, annual wind-induced undercatch ranges from 5 to 9.5%, depending on correction methodology. The highest bias is observed for the solid precipitation – from 17.7 to 27.4%. The precipitation loss increases along with annual wind speed at the weather station (correlation coefficient r = 0.89). Conclusions. We suggest that Golubev’s and Yang’s methodologies estimate precipitation wind-induced undercatch more accurately at stations where blizzards are often observed, we recommended using the Golubev’s methodology because it takes into account “false” precipitations. The precipitation loss equals 0.2–4% according to the Golubev’s method at covered weather stations and reaches 13–19% at the bare mountain regions or seashore. Solid precipitation is more sensitive to the influence of wind – snow loss averages 17.3% according to the Golubev methodology or 21% according to the Yang methodology, while rain loss – 2.6% or 6.7%, respectively. The obtained database with corrected precipitation comprises sub-daily and daily records from 207 Ukrainian stations between 1976 and 2019. It could be used for hydrological and climatological research.

Groundwater recharge rate estimation using remotely sensed and ground-based data: A method application in the mesoscale Thur catchment

Study regionThe mesoscale Thur catchment located in the north‐eastern part of Switzerland is a study site in which a range of hydrological research has been conducted. Study focusGroundwater recharge is challenging to quantify due to the complexity of hydrogeological processes and limited observations. Because of its spatiotemporal availability, remotely sensed data present an attractive water management tool. Its application in mesoscale catchments (10 – 104 km2) however, remains limited. This study investigated the use of satellite products used in conjunction with ground-based data to determine spatiotemporal variations in water available for groundwater recharge. Gridded components from readily available precipitation, evapotranspiration, and hydrological discharge data were used to generate spatiotemporal maps over a 20-year period. New hydrological insights for the regionClosure of the water balance found that monthly data displayed a moderate correlation, with improved correlations of obtained seasonal and annual intervals, suggesting that over the long-term the Thur catchment is in a steady state. Maps were generated and trends for the 20-year period assessed. Examination of the gridded water balance components for different hydrological years emphasized the limiting effect of precipitation on recharge. This study highlights the value of remotely sensed data in groundwater recharge estimates, but emphasises the importance of continued ground-based monitoring; the lack of which is a limiting factor in water management where mesoscale catchments are concerned.

Effects of biological soil crusts on the initial abstraction ratio of SCS-CN model in the Loess Plateau

Hydrological model is an effective tool for hydrological research. The initial abstraction ratio (λ) is a key parameter of SCS-CN model, a commonly used runoff model of great significance to simulate the hydrological process at the watershed scale. In order to examine the effects of biological soil crusts (biocrusts) on λ and improve the accuracy of the model used in the restored grasslands where biocrusts widely presented in the Loess Plateau region, we firstly determined the relationship between the amount of the potential maximum infiltration (S) and the amount of the actual infiltration (F), and then investigated the effects of biocrust coverage on λ by using the simulated rainfall experiment in the Yingwoshanjian watershed in Dingbian County, Shaanxi Province. The revised model was verified by the runoff results of the simulated rainfall experiments in the Zhifanggou watershed in Ansai County, Shaanxi Province. The results showed that the relationship between S and F on biocrust slope was described as S/F=2.5×60/T (where T was the rainfall duration). There was a negative correlation between λ and biocrust coverage (CBSC) described as λ=0.0791×e(-0.015×CBSC), R2=0.60. Compared with that using the standard value of λ, the efficiency coefficient of the model was increased by 338.7% and the qualified rate was increased by 16.1% after revising λ according to the biocrust coverage. The results provided a scientific basis for the calibration of λ on biocrust slopes in the Loess Plateau region, and were of great significance to accurately assess the hydrological effects of the implementation of the "Grain for Green" Program on the Loess Plateau.

Toward Practical Education and Research in Hydrology and Water Resources

Toward Practical Education and Research in Hydrology and Water Resources

Progress and challenges in dual- and triple-isotope (δ18 O, δ2 H, Δ17 O) analyses of environmental waters: An international assessment of laboratory performance.

Stable isotope analyses of environmental waters (δ2 H, δ18 O) are an important assay in hydrology and environmental research with rising interest in δ17 O, which requires ultra-precise assays. We evaluated isotope analyses of six test water samples for 281 laboratory submissions measuring δ2 H and δ18 O along with a subset analyzing δ17 O and Δ17 O by laser spectrometry and isotope ratio mass spectrometry (IRMS). Six test waters were distributed to laboratories spanning a wide δ range of natural waters for δ2 H, δ18 O and δ17 O and Δ17 O. One sample was a blind duplicate to test reproducibility and claims of analytical precision. Results showed that ca 83% of the submissions produced acceptable δ18 O and δ2 H results within 0.2‰ (mUr) and 1.6‰ of the benchmark values, respectively. However, 17% of the submissions gave questionable to unacceptable results. A blind duplicate revealed many laboratories reported overly optimistic precision, and many could not replicate within their claimed precision. For Δ17 O, dual-inlet results for IRMS using quantitative O2 conversion were accurate and highly precise, but the results for laser spectrometry ranged by ca 200 per meg (μUr) for each sample, with ca 70% unable to replicate the duplicate to their claimed Δ17 O precision. One complicating factor is the lack of certified primary reference waters for δ17 O. No single factor or combination of factors was identifiable for poor or good performance, and underperformance came from issues like data normalization including inadequate memory and drift corrections, compromised working reference materials and underperforming instrumentation. We recommend isotope laboratories include high and low δ value controls of known isotope composition in each run. Progress in Δ17 O analyses by laser spectrometry requires extraordinary proof of performance claims and would benefit from the development of adoptable and systematic advanced data processing procedures to correct for memory and drift.

On the hydrological difference between catchments above and below the intermittent‐persistent snow transition

AbstractBecause of the importance of snow for river discharge in mountain regions, hydrological research often focuses on seasonally snow‐covered zones. However, in many basins the majority of the land surface area is intermittently snow‐covered. Discharge monitoring in these areas is less common, so their contributions to downstream rivers remain largely unknown. We evaluated hydrological differences between three intermittently snow‐covered (mean annual Jan 1–Jul 3 snow persistence <60%) and two seasonally snow‐covered headwater catchments in the Colorado Front Range. We compared water balance variables to evaluate how and why discharge differs between the snow zones and estimated the relative contributions from each snow zone to regional river discharge. We focused on water years 2016–2019 and used a combination of in situ sensors and regional climate datasets. Annual discharge from the intermittent snow zone was low for all three catchments (10–77 mm), despite covering a wide range in annual snow persistence (25%–64%), whereas annual discharge from the seasonal snow zone was up to 73 times higher. Soil moisture in the seasonal snow zone was above field capacity for longer periods of time than in the intermittent snow zone, and the intermittent snow zone was uniquely subject to soil freezing (up to 102 days per year). For most of the year, potential evapotranspiration exceeded rainfall and snowmelt inputs in the intermittent snow zone, but was lower than rainfall and snowmelt inputs in the seasonal snow zone. This is likely a primary driver of the differences in soil moisture and discharge for catchments with a seasonal versus intermittent snow cover. Despite the large difference in discharge between these two snow zones, the intermittent snow zone contributed about a quarter of the discharge in the regional river, highlighting the importance of studying discharge generation across all elevations.

Optimization of Rain Gauge Networks for Arid Regions Based on Remote Sensing Data

Water depletion is a growing problem in the world’s arid and semi-arid areas, where groundwater is the primary source of fresh water. Accurate climatic data must be obtained to protect municipal water budgets. Unfortunately, the majority of these arid regions have a sparsely distributed number of rain gauges, which reduces the reliability of the spatio-temporal fields generated. The current research proposes a series of measures to address the problem of data scarcity, in particular regarding in-situ measurements of precipitation. Once the issue of improving the network of ground precipitation measurements is settled, this may pave the way for much-needed hydrological research on topics such as the spatiotemporal distribution of precipitation, flash flood prevention, and soil erosion reduction. In this study, a k-means cluster analysis is used to determine new locations for the rain gauge network at the Eastern side of the Gulf of Suez in Sinai. The clustering procedure adopted is based on integrating a digital elevation model obtained from The Shuttle Radar Topography Mission (SRTM 90 × 90 m) and Integrated Multi-Satellite Retrievals for GPM (IMERG) for four rainy events. This procedure enabled the determination of the potential centroids for three different cluster sizes (3, 6, and 9). Subsequently, each number was tested using the Empirical Cumulative Distribution Function (ECDF) in an effort to determine the optimal one. However, all the tested centroids exhibited gaps in covering the whole range of elevations and precipitation of the test site. The nine centroids with the five existing rain gauges were used as a basis to calculate the error kriging. This procedure enabled decreasing the error by increasing the number of the proposed gauges. The resulting points were tested again by ECDF and this confirmed the optimum of thirty-one suggested additional gauges in covering the whole range of elevations and precipitation records at the study site.

FY-3A/MERSI precipitable water vapor reconstruction and calibration using multi-source observation data based on a generalized regression neural network

Precipitable water vapor (PWV) is a key element in the water and energy transfer between the surface and the atmosphere, especially in the Three-River Headwaters (TRH), which exhibits sensitive climate and hydrology. To overcome the limitations of missing data and poor accuracy of the moderate resolution spectral imager (MERSI) on-board the second-generation polar-orbiting satellite FengYun-3A (FY-3A) in the TRH region, a model for FY-3A/MERSI PWV reconstruction, based on generalized regression neural network (GRNN) using meteorological factors, normalized differential vegetation index (NDVI) and digital elevation model (DEM), is established, and then, a method for daily systematic error of MERSI PWV calibrating based on global navigation satellite system (GNSS)-derived PWV is proposed. The root mean square (RMS), standard deviation (Std), and bias values of the reconstructed PWV are 0.57, 0.52, and 0.02 mm compared with the original MERSI PWV. The RMS, Std and bias of the calibrated MERSI PWV are 2.42, 1.92 and 0.14 mm compared with GNSS-derived PWV, and the correlation coefficient between MERSI and GNSS-derived PWV is improved from 0.78 to 0.95, which is satisfactory. Compared with the original MERSI PWV, the proposed reconstruction and calibration model can fill the missing data and effectively improve the accuracy of the MERSI PWV products. The proposed method can provide a basic PWV data ensuring for hydrological and ecological research in the TRH region.

The Bellinge data set: open data and models for community-wide urban drainage systems research

Abstract. This paper describes a comprehensive and unique open-access data set for research within hydrological and hydraulic modelling of urban drainage systems. The data come from a mainly combined urban drainage system covering a 1.7 km2 area in the town of Bellinge, a suburb of the city of Odense, Denmark. The data set consists of up to 10 years of observations (2010–2020) from 13 level meters, 1 flow meter, 1 position sensor and 4 power sensors in the system, along with rainfall data from three rain gauges and two weather radars (X- and C-band), and meteorological data from a nearby weather station. The system characteristics of the urban drainage system (information about manholes, pipes, etc.) can be found in the data set along with characteristics of the surface area (contour lines, surface description, etc.). Two detailed hydrodynamic, distributed urban drainage models of the system are provided in the software systems MIKE URBAN and EPA Storm Water Management Model (SWMM). The two simulation models generally show similar responses, but systematic differences are present since the models have not been calibrated. With this data set we provide a useful case that will enable independent testing and replication of results from future scientific developments and innovation within urban hydrology and urban drainage systems research. The data set can be downloaded from https://doi.org/10.11583/DTU.c.5029124 (Pedersen et al., 2021a).

Determining Water Isotope Compositions for the IAEA WICO and North West Villages, South Africa

Deuterium (2H) and oxygen 18 (18O) stable isotopes in water are the key indicators of hydrological and ecological patterns and processes. The water isotopes δ2H and δ18O have been employed widely as tracers in hydrological and ecological research, as they are integrated into geological and biological systems in a predictable manner. The aim of this study was to determine the water isotope composition of the International Atomic Energy Agency’s (IAEA) Water Isotope Inter-laboratory Comparison (WICO) samples and to determine the Local Meteoric Water Line for North West Province (NWP) villages in South Africa. The IAEA WICO 2020 samples were obtained from the IAEA, Vienna, and borehole water samples from selected villages in the North West province of South Africa were randomly collected to investigate the relationship between the stable isotopes (18O and 2H) and the climate in underground water aquifers. A cavity ring-down spectroscopy analyzer with laser-current-tuned cavity resonance, Picarro L2140-i, was used to measure triple water–isotope ratios with high precision. The IAEA WICO results obtained for the d-excess were in a satisfactory range and the margins of error were close to those required by the IAEA. The δ2H values ranged between −33.00‰ and −8.00‰, while the δ18O values ranged between −5.50‰ and −2.00‰. The results of this work have shown that our water science and management lab is capable of undertaking inter-laboratory comparisons for the IAEA.

Achieving Breakthroughs in Global Hydrologic Science by Unlocking the Power of Multisensor, Multidisciplinary Earth Observations

AbstractOver the last half century, remote sensing has transformed hydrologic science. Whereas early efforts were devoted to observation of discrete variables, we now consider spaceborne missions dedicated to interlinked global hydrologic processes. Furthermore, cloud computing and computational techniques are accelerating analyses of these data. How will the hydrologic community use these new resources to better understand the world's water and related challenges facing society? In this commentary, we suggest that optimizing the benefits of remote sensing for advancing hydrologic research will happen by integrating multidisciplinary and multisensor data, leveraging commercial satellite measurements, and employing data assimilation, cloud computing, and machine learning. We provide several recommendations to these ends.

Extent of detection of hidden relationships among different hydrological variables during floods using data-driven models.

Understanding of flood dynamics forms the basis for the leading water resource management and flood risk mitigation practices. In particular, accurate prediction of river flow during massive flood events and capturing the hysteretic behavior of river stage-discharge are among the key interests in hydrological research. The literature demonstrates that data-driven models are significant in identifying complex and hidden relationships among dependent variables, without considering explicit physical schemes. In this regard, we aim to discover the extent to which data-driven models can recognize the hidden relationships among different hydrological variables, in order to generate accurate predictions of the river flow. A secondary aim involves the detection of whether data-driven models can digest the internal features of training inputs to extrapolate severe flood records beyond the training domain. To achieve these aims, we developed a recurrent neural network (RNN) model of two hidden layers to capture the hidden relationships among the inputs, and investigated the model's predictive capability using quantitative and qualitative analyses. The quantitative analysis comprised of a comparison between model predictions, and another set of precise independent records obtained through an advanced hydroacoustic system for reference. A qualitative approach was adopted to visualize the hysteretic behavior of the stage-discharge relations of the model records, with the high-resolution records of the hydroacoustic system. The findings display the potential of data-driven models for accurately predicting river flow. Consequently, the qualitative analysis revealed moderate correlations of stage-discharge loops as compared to the reference records. Additionally, the model was tested against severe destructive flood records generated from the East Asian monsoon and tropical cyclones. Its findings suggest that data-driven models cannot extrapolate new features beyond their training dataset. Overall, this study discusses the competence of RNNs in providing reliable and accurate river flow predictions during floods.