Streamflow In Ungauged Basins Research Articles

Improved National‐Scale Above‐Normal Flow Prediction for Gauged and Ungauged Basins Using a Spatio‐Temporal Hierarchical Model

AbstractFloods cause hundreds of fatalities and billions of dollars of economic loss each year in the United States. To mitigate these damages, accurate flood prediction is needed for issuing early warnings to the public. This situation is exacerbated in larger model domains flood prediction, particularly in ungauged basins. To improve flood prediction for both gauged and ungauged basins, we propose a spatio‐temporal hierarchical model (STHM) using above‐normal flow estimation with a 10‐day window of modeled National Water Model (NWM) streamflow and a variety of catchment characteristics as input. The STHM is calibrated (1993–2008) and validated (2009–2018) in controlled, natural, and coastal basins over three broad groups, and shows significant improvement for the first two basin types. A seasonal analysis shows the most influential predictors beyond NWM streamflow reanalysis are the previous 3‐day average streamflow and the aridity index for controlled and natural basins, respectively. To evaluate the STHM in improving above‐normal streamflow in ungauged basins, 20‐fold cross‐validation is performed by leaving 5% of sites. Results show that the STHM increases predictive skill in over 50% of sites' by 0.1 Nash‐Sutcliffe efficiency (NSE) and improves over 65% of sites' streamflow prediction to an NSE > 0.67, which demonstrates that the STHM is one of the first of its kind and could be employed for flood prediction in both gauged and ungauged basins.

Novel Approaches for Regionalising SWAT Parameters Based on Machine Learning Clustering for Estimating Streamflow in Ungauged Basins

Novel Approaches for Regionalising SWAT Parameters Based on Machine Learning Clustering for Estimating Streamflow in Ungauged Basins

Ecohydrologic model with satellite-based data for predicting streamflow in ungauged basins

Information on water availability in basins can be crucial for making decisions for effective water resource management in basins. As the operation of hydrometric stations in Korea is mainly focused on flood season and large rivers, most basins have lack or no observed data. Consequently, this complicates water resource planning and management. Remote sensing data is emerging as a powerful alternative to hydrological information in ungauged basins. This study investigated the applicability of Satellite-Remote Sensed Data (SRSD) as a source for model calibration in Prediction in Ungauged Basins (PUB) through modeling. Remote sensed leaf area index (LAI), actual evapotranspiration, and soil moisture data were used. Each SRSD was used alone to calibrate a hydrologic model to predict the daily streamflow for 28 basins in Korea. A vegetation module was added to the existing hydrologic model to use LAI. Among the SRSDs tested, the model calibrated with LAI had the most robust performance, predicting streamflow with acceptable accuracy compared to the traditional calibration based on streamflow. In particular, since the model account for vegetation actively interacting with evapotranspiration and soil moisture in the season of low flow, the LAI-calibrated model showed an advantage in improving the flow prediction performance. Although further research is required to utilize evapotranspiration and soil moisture data, the overall results of the LAI-based calibration were promising for predicting streamflow in ungauged basins where observations are scarce or absent, given that the satellite-derived LAI data were used alone without any preprocessing such as a bias correction. However, the prediction performance of the LAI-calibrated model was found to have a statistically significant relationship with local conditions. Therefore, by evaluating and improving the potential of SRSD in different region and climatic conditions, it is expected that the application of the SRSD-only calibration method can be extended to various ungauged basins.

Predicting streamflow with LSTM networks using global datasets

Streamflow predictions remain a challenge for poorly gauged and ungauged catchments. Recent research has shown that deep learning methods based on Long Short-Term Memory (LSTM) cells outperform process-based hydrological models for rainfall-runoff modeling, opening new possibilities for prediction in ungauged basins (PUB). These studies usually feature local datasets for model development, while predictions in ungauged basins at a global scale require training on global datasets. In this study, we develop LSTM models for over 500 catchments from the CAMELS-US data base using global ERA5 meteorological forcing and global catchment characteristics retrieved with the HydroMT tool. Comparison against an LSTM trained with local datasets shows that, while the latter generally yields superior performances due to the higher spatial resolution meteorological forcing (overall median daily NSE 0.54 vs. 0.71), training with ERA5 results in higher NSE in most catchments of Western and North-Western US (median daily NSE of 0.83 vs. 0.78). No significant changes in performance occur when substituting local with global data sources for deriving the catchment characteristics. These results encourage further research to develop LSTM models for worldwide predictions of streamflow in ungauged basins using available global datasets. Promising directions include training the models with streamflow data from different regions of the world and with higher quality meteorological forcing.

Utilization of the Long Short-Term Memory network for predicting streamflow in ungauged basins in Korea

Hydrological prediction is essential for managing and preserving headwater, wetland, and rural basins, yet it is difficult due to a lack of data. The Long Short-Term Memory (LSTM) network is a promising deep learning approach and has demonstrated excellent performance in streamflow prediction. However, due to the characteristics that require abundant and high-quality observations, most LSTM applications focus on investigating performance for data-rich basins. To investigate the applicability of LSTM in ungauged basins without hydrological observations, we introduce and test an approach to predict streamflow in ungauged basins using the LSTM network that has learned the integration of data from multiple gauged basins. Four learning strategies are constructed using various datasets such as ground-observed meteorological data, satellite data, and hydro-geomorphological characteristics. As a result, the LSTM network that learned the meteorological data in multiple gauged basins satisfactorily predicted streamflow in ungauged basins. The LSTM network that learned the satellite data further showed affirmative results. On the other hand, the LSTM, which additionally learned the hydro-geomorphological characteristics of the basins, exposed the need for improvement, such as securing additional various types of training data. In addition, it was recognized that additional efforts were needed to solve overfitting and out-of-distribution prediction problems. However, this approach achieved model performance deriving metrics above threshold values. These results show the potential of LSTM for streamflow prediction in ungauged basins including wetlands.

Daily streamflow prediction in ungauged basins: an analysis of common regionalization methods over the African continent

ABSTRACT Predicting streamflow in ungauged basins is a critical task in many projects. This study investigates the applicability and performance of three common regionalization methods over 314 catchments distributed throughout Africa, using the HMETS hydrological model. This study finds that regionalization methods that work well in other regions of the world also do so in the African catchments, except for multiple linear regression which performs worse than expected. Furthermore, skill-based donor filtering is shown to be problematic in some circumstances, notably when the filter rejects mediocre but still useful donors. This study shows that regionalization performance depends more on the hydrological regime of the ungauged catchment and the capacity of hydrological models to adapt to that regime, rather than on any physiographic or climatological metric. In the context of ungauged basins, this indicates that there is no easy recipe to predict the skill of regionalization methods on any given catchment.

Usefulness of Global Root Zone Soil Moisture Product for Streamflow Prediction of Ungauged Basins

Using modelling approaches to predict stream flow from ungauged basins requires new model calibration strategies and evaluation methods that are different from the existing ones. Soil moisture information plays an important role in hydrological applications in basins. Increased availability of remote sensing data presents a significant opportunity to obtain the predictive performance of hydrological models (especially in ungauged basins), but there is still a limit to applying remote sensing soil moisture data directly to models. The Soil Moisture Active Passive (SMAP) satellite mission provides global soil moisture data estimated by assimilating remotely sensed brightness temperature to a land surface model. This study investigates the potential of a hydrological model calibrated using only global root zone soil moisture based on satellite observation when attempting to predict stream flow in ungauged basins. This approach’s advantage is that it is particularly useful for stream flow prediction in ungauged basins since it does not require observed stream flow data to calibrate a model. The modelling experiments were carried out on upstream watersheds of two dams in South Korea with high-quality stream flow data. The resulting model outputs when calibrated using soil moisture data without observed stream flow data are particularly impressive when simulating monthly stream flows upstream of the dams, and daily stream flows also showed a satisfactory level of predictive performance. In particular, the model calibrated using soil moisture data for dry years showed better predictive performance than for wet years. The performance of the model calibrated using soil moisture data was significantly improved under low flow conditions compared to the traditional regionalization approach. Additionally, the overall stream flow was also predicted better. In addition, the uncertainty of the model calibrated using soil moisture was not much different from that of the model calibrated using observed stream flow data, and showed more robust outputs when compared to the traditional regionalization approach. These results prove that the application of the global soil moisture product for predicting stream flows in ungauged basins is promising.

Spatiotemporal assimilation–interpolation of discharge records through inverse streamflow routing

Abstract. Poorly monitored river flows in many regions of the world have been hindering our ability to accurately estimate global water budgets as well as the variability of the global water cycle. In situ gauging sites, as well as a number of satellite-based systems, make observations of river discharge throughout the globe; however, these observations are often sparse due to, for example, the sampling frequencies of sensors or a lack of reporting. Recently, efforts have been made to develop methods to integrate these discrete observations to gain a better understanding of the underlying processes. This paper presents an application of a fixed interval Kalman smoother-based model, called inverse streamflow routing (ISR), to generate spatially and temporally continuous river discharge fields from discrete observations. The method propagates the observed information across all reachable parts of the river network (up/downstream from gauging point) and all reachable times (before/after observation time) using a two-sweep procedure that first propagates information backward in time to the furthest upstream locations (inverse routing) and then propagates it forward in time to the furthest downstream locations (forward routing). The ISR methodology advances prediction of streamflow in ungauged basins by accounting for a physical representation of the river system that is not generally handled explicitly in more-commonly applied statistically based models. The key advantages of this approach are that it (1) maintains all the physical consistencies embodied by a diffusive wave routing model (flow confluence relationships on the river network and the resulting mass balance, wave velocity, and diffusivity), (2) updates the lateral influx (runoff) at the pixel level (furthest upstream) to guarantee exhaustive propagation of observed information, and (3) works both with a first guess of initial river discharge conditions from a routing model (assimilation) and without a first guess (pure interpolation of observations). Two sets of experiments are carried out under idealized conditions and under real-world conditions provided by United States Geological Survey (USGS) observations. Results show that the method can effectively reproduce the spatial and temporal dynamics of river discharge in each of the experiments presented. The performance is driven by the density of the gauge network as well as the quality of the data being assimilated. We find that when assimilating the actual USGS observations, the performance decreases relative to our idealized scenario; however, we are still able to produce an improved discharge product at each validation site. With further testing, as well as global application, ISR may prove to be a useful method for extending our current network of global river discharge observations.

Streamflow regionalization using a similarity approach in ungauged basins: Application of the geo-environmental signatures in the Karkheh River Basin, Iran

Reliable estimation of streamflow especially in ungauged basins is most important for environmental management and planning. Regionalization of streamflow using either the physical attributes or spatial proximities of linked gauged and ungauged basins are considered to be standard techniques for estimating streamflow in ungauged basins. Unfortunately, regionalization of modeling parameters based solely on physical similarities or spatial proximity between gauged and ungauged basins is not always adequate. The hydrologic similarities between basins are related to the basin's response to precipitation, which it is referred to various conditions of basins, so considering the comprehensive characteristics from basins is very important to achieve a knowledge of the hydrology of the basin. Therefore, this study aimed to consider a comprehensive set of signatures with the fuzzy clustering approach. Similar basins were determined by considering key geo-environmental signatures, including topographical/morphological characteristics, soil properties, land use types, remote sensing (RS)-based indices, and climate indices. Redundancy in the selected geo-environmental signatures was assessed using factor analysis. Parameters of lumped and semi-distributed rainfall-runoff models were calibrated in the most similar donor basin (DB) to the ungauged basin (UB) and validated by matching them to the pseudo ungauged basin (PUB). Streamflow estimation results from the pseudo ungauged basins demonstrated that the applied procedure and semi-distributed rainfall-runoff model was an efficient method to estimate streamflow in the ungauged basins.

How Does Snow Persistence Relate to Annual Streamflow in Mountain Watersheds of the Western U.S. With Wet Maritime and Dry Continental Climates?

AbstractWith climate warming, many regions are experiencing changes in snow accumulation and persistence. These changes are known to affect streamflow volume, but the magnitude of the effect varies between regions. This research evaluates whether variables derived from remotely sensed snow cover can be used to estimate annual streamflow at the small watershed scale across the western U.S., a region with a wide range of climate types. We compared snow cover variables derived from MODIS, snow persistence (SP), and snow season (SS), to more commonly utilized metrics, snow fraction (fraction of precipitation falling as snow, SF), and peak snow water equivalent (SWE). Each variable represents different information about snow, and this comparison assesses similarities and differences between the snow metrics. Next, we evaluated how two snow variables, SP and SWE, related to annual streamflow (Q) for 119 USGS reference watersheds and examined whether these relationships varied for wet/warm (precipitation surplus) and dry/cold (precipitation deficit) watersheds. Results showed high correlations between all snow variables, but the slopes of these relationships differed between climates, with wet/warm watersheds displaying lower SF and higher SWE for the same SP. In dry/cold watersheds, both SP and SNODAS SWE correlated with Q spatially across all watersheds and over time within individual watersheds. We conclude that SP can be used to map spatial patterns of annual streamflow generation in dry/cold parts of the region. Applying this approach to the Upper Colorado River Basin demonstrates that 50% of streamflow comes from areas >3,000 masl. If the relationship between SP and Q is similar in other dry/cold regions, this approach could be used to estimate annual streamflow in ungauged basins.

Simulating streamflow in ungauged basins under a changing climate: The importance of landscape characteristics

In this paper we explored how landscape characteristics such as topography, geology, soils and land cover influence the way catchments respond to changing climate conditions. Based on an ensemble of 15 regional climate models bias-corrected with a distribution-mapping approach, present and future streamflow in 14 neighboring and rather similar catchments in Northern Sweden was simulated with the HBV model. We established functional relationships between a range of landscape characteristics and projected changes in streamflow signatures. These were then used to analyze hydrological consequences of physical perturbations in a hypothetically ungauged basin in a climate change context. Our analysis showed a strong connection between the forest cover extent and the sensitivity of different components of a catchment’s hydrological regime to changing climate conditions. This emphasizes the need to redefine forestry goals and practices in advance of climate change-related risks and uncertainties.

Watershed Classification Using Isomap Technique and Hydrometeorological Attributes

AbstractClassification of watersheds into hydrologically similar groups prior to regionalization is essential for predicting streamflow in ungauged basins. The objective of this study was to improv...

Continuous streamflow prediction in ungauged basins: The effects of equifinality and parameter set selection on uncertainty in regionalization approaches

AbstractThis paper focuses on evaluating the uncertainty of three common regionalization methods for predicting continuous streamflow in ungauged basins. A set of 268 basins covering 1.6 million km2 in the province of Quebec was used to test the regionalization strategies. The multiple linear regression, spatial proximity, and physical similarity approaches were evaluated on the catchments using a leave‐one‐out cross‐validation scheme. The lumped conceptual HSAMI hydrological model was used throughout the study. A bootstrapping method was chosen to further estimate uncertainty due to parameter set selection for each of the parameter set/regionalization method pairs. Results show that parameter set selection can play an important role in regionalization method performance depending on the regionalization methods (and their variants) used and that equifinality does not contribute significantly to the overall uncertainty witnessed throughout the regionalization methods applications. Regression methods fail to consistently assign behavioral parameter sets to the pseudoungauged basins (i.e., the ones left out). Spatial proximity and physical similarity score better, the latter being the best. It is also shown that combining either physical similarity or spatial proximity with the multiple linear regression method can lead to an even more successful prediction rate. However, even the best methods were shown to be unreliable to an extent, as successful prediction rates never surpass 75%. Finally, this paper shows that the selection of catchment descriptors is crucial to the regionalization strategies' performance and that for the HSAMI model, the optimal number of donor catchments for transferred parameter sets lies between four and seven.

Numerical investigations on the relationships between watershed characteristics and water balance model parameters: searching for universal relationships among regional relationships

AbstractThe present study attempts to identify universal relationships between the geographical characteristics of a basin and the streamflow within a basin by using the parameter regionalization method of a water balance model, with the aim of gaining hydrological knowledge that might enable predictions of streamflow in ungauged basins. To this end, we first revisited existing regional relationships between the parameters of a Tank model and the geographical characteristics in Japanese mountainous basins, and translated the regional relationships between the geographical characteristics of a basin and streamflow. Second, we used a physically distributed rainfall–runoff model to perform a sensitivity analysis on the effect of the geographical characteristics of a hypothetical basin on its streamflow, with the aim of obtaining physically explainable relationships between these factors. Finally, we selected regional relationships that were also explained in the sensitivity analysis as potentially representing universal hydrological knowledge that might help to predict streamflow in ungauged basins. As a result, we obtained the following potentially universal hydrological knowledge: (1) the fraction of surface flow is less in large basins than in small basins; (2) the infiltration at the ground surface is higher if a basin is covered by soil with a high vertical permeability; (3) the fraction of prompt interflow is higher if a basin is covered by soil with a high horizontal permeability; (4) the fraction of delayed interflow is lower in large basins than in small basins and (5) the storage capacity at the ground surface is high if the ground surface is very rough. Copyright © 2011 John Wiley & Sons, Ltd.

Prediction of Streamflow Regime and Annual Runoff for Ungauged Basins Using a Distributed Monthly Water Balance Model1

Moore, R.D. (Dan), J.W. Trubilowicz, and J.M. Buttle, 2011. Prediction of Streamflow Regime and Annual Runoff for Ungauged Basins Using a Distributed Monthly Water Balance Model. Journal of the American Water Resources Association (JAWRA) 48(1): 32‐42. DOI: 10.1111/j.1752‐1688.2011.00595.xAbstract: Prediction of streamflow in ungauged basins is a global challenge, but is particularly an issue in physiographically complex regions like British Columbia (BC), Canada. The objective of this study was to assess the accuracy of a simple water balance model that can be run using existing spatial datasets. The model was developed by modifying an existing monthly water balance model to account for interception loss from forest canopy, glacier melt, and evaporation from lakes. The model was run using monthly climate normals from the ClimateBC application, which have a horizontal resolution of 400 m. Each ClimateBC grid cell was classified as forest, open land, glacier or water surface based on provincial scale digital maps of biogeoclimatic zones, glaciers, and water. The output was monthly mean runoff from each grid cell. These values were integrated within the catchment boundaries for streams gauged by the Water Survey of Canada. Annual runoff was predicted with modest accuracy: after updating the predicted runoff by interpolating errors from neighboring gauged streams, the mean absolute error was 25.4% of the gauged value, and 52% of the streams had errors less than 20%. However, the model appears to be quite robust in distinguishing between pluvial, hybrid, and melt‐dominated hydroclimatic regimes, and therefore has promise as a tool for catchment classification.

Advances in ungauged streamflow prediction using artificial neural networks

Summary In this work, we develop and test two artificial neural networks (ANNs) to forecast streamflow in ungauged basins. The model inputs include time-lagged records of precipitation and temperature. In addition, recurrent feedback loops allow the ANN streamflow estimates to be used as model inputs. Publically available climate and US Geological Survey streamflow records from sub-basins in Northern Vermont are used to train and test the methods. Time-series analysis of the climate-flow data provides a transferable and systematic methodology to determine the appropriate number of time-lagged input data. To predict streamflow in an ungauged basin, the recurrent ANNs are trained on climate-flow data from one basin and used to forecast streamflow in a nearby basin with different (more representative) climate inputs. One of the key results of this work, and the reason why time-lagged predictions of steamflow improve forecasts, is these recurrent flow predictions are being driven by time-lagged locally-measured climate data. The successful demonstration of these flow prediction methods with publicly available USGS flow and NCDC climate datasets shows that the ANNs, trained on a climate-discharge record from one basin, prove capable of predicting streamflow in a nearby basin as accurately as in the basin on which they were trained. This suggests that the proposed methods are widely applicable, at least in the humid, temperate climate zones shown in this work. A scaling ratio, based on a relationship between bankfull discharge and basin drainage area, accounts for the change in drainage area from one basin to another. Hourly streamflow predictions were superior to those using daily data for the small streams tested due the loss of critical lag times through upscaling. The ANNs selected in this work always converge, avoid stochastic training algorithms, and are applicable in small ungauged basins.

Prediction of daily flow duration curves and streamflow for ungauged catchments using regional flow duration curves

A method is presented to predict flow duration curves (FDCs) and streamflow for ungauged catchments in the Mid-Atlantic Region, USA. Twenty-nine catchments were selected from the Appalachian Plateau, Ridge and Valley, and Piedmont physiographic provinces to develop and test the proposed method. Using a step-wise multiple regression analysis, the dominant landscape and climate descriptors were identified and regional FDC models were developed for each province. Predictive performance of the proposed method was estimated using data from three evaluation sites that were not included in parameter estimation for the regional FDC models. The results of this study show that climate and geomorphological descriptors strongly influence the hydrology of the Appalachian Plateau and some Ridge and Valley catchments, whereas soil and geomorphological descriptors strongly influence the hydrology of the Piedmont and some Ridge and Valley catchments. Streamflow values calculated by the drainage area ratio method and those reconstructed by the FDC method were compared with the observed streamflow values. The results indicate that the FDC-based method shows great promise for predicting streamflow in ungauged basins.

Improved unit hydrograph identification for seven Welsh rivers: implications for estimating continuous streamflow at ungauged sites

A unit hydrograph-based continuous rainfall—streamflow model is applied at daily time step to several catchments in Wales. Two calibration methods are compared for seven catchments. Five of the catchments are among 60 basins in England and Wales for which models were derived by other investigators using the first calibration method, in order to devise a regionalization scheme allowing model parameters to be estimated from catchment properties. The paper shows that in several cases the first calibration method tends to overestimate low flows and give a positively biased estimate for one of the unit hydrograph parameters. It also shows that the second parameter calibration method can often overcome this problem, yielding model parameters more suitable for regionalization with respect to physical catchment properties. Implications of the work are discussed in the context of improving regionalization schemes where the estimation of streamflow in ungauged basins is the major objective.

Bayesian estimation of parameters in a regional hydrological model

Abstract. This study evaluates the applicability of the distributed, process-oriented Ecomag model for prediction of daily streamflow in ungauged basins. The Ecomag model is applied as a regional model to nine catchments in the NOPEX area, using Bayesian statistics to estimate the posterior distribution of the model parameters conditioned on the observed streamflow. The distribution is calculated by Markov Chain Monte Carlo (MCMC) analysis. The Bayesian method requires formulation of a likelihood function for the parameters and three alternative formulations are used. The first is a subjectively chosen objective function that describes the goodness of fit between the simulated and observed streamflow, as defined in the GLUE framework. The second and third formulations are more statistically correct likelihood models that describe the simulation errors. The full statistical likelihood model describes the simulation errors as an AR(1) process, whereas the simple model excludes the auto-regressive part. The statistical parameters depend on the catchments and the hydrological processes and the statistical and the hydrological parameters are estimated simultaneously. The results show that the simple likelihood model gives the most robust parameter estimates. The simulation error may be explained to a large extent by the catchment characteristics and climatic conditions, so it is possible to transfer knowledge about them to ungauged catchments. The statistical models for the simulation errors indicate that structural errors in the model are more important than parameter uncertainties. Keywords: regional hydrological model, model uncertainty, Bayesian analysis, Markov Chain Monte Carlo analysis