Ungauged Basins Research Articles

Estimation of inflow discharge to Lake Baikal at upstream section using different satellite-based precipitation and runoff datasets from Upper Angara and Kichera River basins in East Siberia, Russia

Accurate basin-level river discharge estimation is of vital importance across various fields, including water resources, climate change, natural hazards, biodiversity, and energy production. Normally, gauging stations are deemed the most reliable data source for measuring river discharge. However, a significant proportion of the world’s rivers remain ungauged due to a combination of technical, economic, and political constraints. Encouragingly, recent advancements in remote sensing and satellite observation have opened new avenues for global river discharge monitoring, even in ungauged basins, and the availability of extensive datasets and advancements in computing technologies have facilitated the development of numerous modern data-driven techniques. The general objective of this study is to estimate inflow discharge to Lake Baikal at upstream section from Upper Angara and Kichera River Basins using different satellite precipitation and runoff datasets. According to the calculation result, a higher discharge was observed for the power dataset. The obtained results were used to mitigate floods, droughts, bridge design, manage urban drainage systems, and manage the lake ecosystem.

Assessing the Impact of Rainfall Inputs on Short-Term Flood Simulation with Cell2Flood: A Case Study of the Waryong Reservoir Basin

This study explored the impacts of various rainfall input types on short-term runoff simulations using the Cell2Flood model in the Waryong Reservoir Basin, South Korea. Six types of rainfall data were assessed: on-site gauge measurements, spatially interpolated data from 39 Automated Synoptic Observing System (ASOS) and 117 Automatic Weather System (AWS) stations using inverse distance weighting (IDW), and Hybrid Surface Rainfall (HSR) data from the Korea Meteorological Administration. The choice of rainfall input significantly affected model accuracy across the three rainfall events. The point-gauged ASOS (P-ASOS) data demonstrated the highest reliability in capturing the observed rainfall patterns, with Pearson’s r values of up to 0.84, whereas the radar-derived HSR data had the lowest correlations (Pearson’s r below 0.2), highlighting substantial discrepancies. For runoff simulation, the P-ASOS and ASOS-AWS combined interpolated dataset (R-AWS) achieved relatively accurate predictions, with P-ASOS and R-AWS exhibiting Normalized Peak Error (NPE) values of approximately 0.03 and Peak Time Error (PTE) within 20 min. In contrast, the HSR data produced large errors, with NPE up to 4.66 and PTE deviations exceeding 200 min, indicating poor temporal accuracy. Although input-specific calibration improved performance, significant errors persisted because of the inherent uncertainty of rainfall data. These findings underscore the importance of selecting and calibrating appropriate rainfall inputs to enhance the reliability of short-term flood modeling, particularly in ungauged and data-sparse basins.

Streamflow Simulation for Nam Xong River Basin in Vang Vieng District, Vientiane Province

Nam Xong River is one of the tributaries of Nam Lik River, which is considered an important river that creates natural tourism value for the people in Vang Vieng District. Nam Xong Dam was another dam built to generate electricity and divert water to Nam Ngum 1 reservoir to increase hydropower generation capacity. Recently, Nam Xong River Basin (NXRB) has been affected by flooding almost every year due to changes in the amount and pattern of rainfall which is highly variable. Another issue is aggravated by the problem of ungauged basins where the number of hydrological and meteorological stations are limited and unequally distributed over the basins. This obstructs the reliability of streamflow prediction and water resources management. This study therefore aims to address the problems of ungauged basins by performing tasks that serve a main objective: application of HEC-HMS for simulating the streamflow for NXRB. The hydrological and meteorological data used in this study are daily streamflow 2 stations and rainfall 4 stations located in and around the basin. The simulation period is considered based on random flood events from 2005 to 2021. Out of these, 5 events (2005-2015) are selected for model calibration and another event of 2021 are considered for model validation. The results found that the HEC-HMS model can estimate the flow rate in accordance with the observed data, especially during the low flow period. Throughout the considered period, the average flow and total flow volume showed a downward trend. For the result of estimating the maximum flow from the model, the streamflow is lower than the observed data, and the time to peak from the model comes about 1 day later than the observed one in 2014, probably due to the distribution of rainfall in the study area with high variability. Therefore, the HEC-HMS model could be used to simulate daily streamflow in limited data areas. This study could provide recommendations for hydrological modeling for other basins that have similar characteristics to the NXRB.

Study on the Available Amounts of Rainwater/ Stormwater Resources in Ungauged Basin in Semi-Arid Region of the Loess Plateau

Study on the Available Amounts of Rainwater/ Stormwater Resources in Ungauged Basin in Semi-Arid Region of the Loess Plateau

Remote sensing and big data: Google Earth Engine data to assist calibration processes in hydro-sediment modeling on large scales

Mathematical modeling aids in understanding large-scale erosion and sedimentation. However, sediment transport models calibration is constrained by data scarcity. This study explores the use of remote sensing (RS) imagery to supplement observed data, addressing three key questions: (1) How can high-resolution RS data be obtained using cloud-based methods for hydro-sediment applications, considering river changes? (2) What are the benefits of RS data in data-scarce conditions? (3) How can RS data improve hydro-sediment modeling in data-deficient regions? We developed a method to acquire large-scale RS data using Google Earth Engine (GEE) to obtain red and infrared reflectance from satellite imagery. After filtering errors, the data were used to calibrate a hydro-sediment model. Results showed that RS data, when combined with observed data, provided similar outcomes but performed better for lower values. Calibration with RS data alone improved the Kling-Gupta Efficiency (KGE) by 5%–18% and correlation by 5%–15%. Key conclusions are: (I) Cloud-based calibration is superior to using limited virtual stations; (II) RS data effectively complements observed data in hydro-sediment modeling; (III) Calibration using only RS data is beneficial in ungauged basins and preferable to no calibration.

Debris flows analysis through quantitative evaluation of soil depth distribution under limited data

Soil depth is essential in studying natural disasters such as landslides and debris flow hazards. Despite the importance of soil depth in the mechanism of erosion-entrainment during the debris flow process, research on soil-depth data for analyzing debris flows is limited. Therefore, this study focused on the Gallam-ri area with a watershed of 0.9 km2 to evaluate the soil depth mapping under limited data and significance of these maps for debris flow simulations. Based on the knocking pole test data, two-dimensional distribution soil depth maps were constructed using the S and Z models, the Kriging method, and a method that applies some values uniformly as the soil depth (U model). The accuracy of soil depth mapping methods was quantitatively evaluated using R2 and root mean squared error analysis. Since soil depth demonstrated independent patterns with land-surface data, soil depth maps using S and Z models have structural limitations showing R2 of 0.0003 and 0.002, respectively. The debris flows were analyzed through numerical model Deb2D, and the soil depth most significantly influenced the erosion volume and the damaged area. Analyses using S and U models showed 94 % and 98 % high similarity to the simulation results through the Kriging method, respectively. However, considering overall analyses, the S model was analyzed to be the most stable in constructing soil depth maps and simulating debris flows for ungauged basins.

Improving Discharge Predictions in Ungauged Basins: Harnessing the Power of Disaggregated Data Modeling and Machine Learning

AbstractCurrent machine learning methods for discharge prediction often employ aggregated basin‐wide hydrometeorological data (lumped modeling) for parametric and non‐parametric training. This approach may overlook the spatial heterogeneity of river systems and their impact on discharge patterns. We hypothesize that integrating spatiotemporal hydrologic knowledge into the data modeling process (distributed/disaggregated modeling) can improve the performance of discharge prediction models. To test this hypothesis, we designed experiments comparing the performance of identical Long Short‐Term Memory Recurrent Neural Network (LSTM‐RNN) models forced with either lumped or distributed features. We gather meteorological forcing and static attributes for the Mackenzie basin in Canada‐ a large and unique basin. Importantly, discharge performance is assessed out‐of‐sample with k‐fold replication across gauges. Training LSTMs with disaggregated data significantly improved model accuracy. Specifically, there was a 9.6% increase in the mean Nash‐Sutcliffe Efficiency and a 4.6% increase in the mean Kling‐Gupta Efficiency, indicating a better agreement between predicted and actual observations in terms of mean, variability, and correlation. These experiments and results demonstrate the importance of integrating topologically guided geomorphologic and hydrologic information (distributed modeling) in data‐driven discharge predictions.

Application of hydrological models to streamflow estimation at ungauged transboundary Himalayan River basin, Nepal

ABSTRACT Continuous hydrological records at the desired site with spatial and temporal coverage are essential to water resources management and flood prevention. Hydrological gauging stations and observations are limited at transboundary Himalayan River basins in developing countries. This study is carried out to estimate discharge at ungauged sites from donor catchments based on calibrated discharge. In the Tamakoshi River basin, Spatial Process in HYdrology (SPHY), Hydrologiska Byråns Vattenbalansavdelning (HBV)-light, and Hydrologic Engineering Centre Hydrologic Modelling System (HEC HMS) models were used for hydrological simulation. The ungauged discharge estimated at Benighat is based on daily, monthly, and annual bases from upstream gauged stations. Statistical indices were obtained as NSE > 0.62, R2 > 0.77, and PBias <26% in the simulation of these models. The low flow prediction is more reliable than the high flow prediction. The deviation in predicted flow mostly appeared in high flow periods. All the hydrological models simulated ungauged streamflow were similar in the flow pattern, and the estimated discharge at the ungauged receiver site was greater than at the donor site. Comparative simulations are better alternatives to estimate ungauged discharge than individual simulations. This research will support future reference to generate streamflow data at ungauged sites and can help fill the data recording gap at similar mountain river basins.

Regional power duration curve model for ungauged intermittent river basins

ABSTRACT Hydropower is a sustainable and renewable energy source that can serve as a practical and economically viable solution to the future possible energy crisis and climate change scenarios. Moreover, it possesses a higher energy density compared to other alternative renewable energy sources such as solar, wind energy, etc. In order to determine the potential of hydropower, long-term observed hydrometeorological data of streamflow, precipitation, etc. are crucial. This study investigates a new power duration curve (PDC) methodology. Basin characteristics such as drainage area and basin relief with meteorological data as precipitation are used for regional models in the application. The classification based on geographical locations is made for regional models. Six models based on equation type were utilized to determine the optimal regional model. Absolute errors of cease-to-flow point estimates ranging from 0.01 to 11.49% were observed. The model provided successful results according to Nash–Sutcliffe efficiency which is widely used in hydrological studies very close to 1 and higher than 0.87 except for one streamflow gauging station therewithal all other calculated performance metrics. It is observed that power and cease-to-flow point estimates of intermittent rivers can be obtained with a new PDC model based on basin characteristics.

Continental Scale Regional Flood Frequency Analysis: Combining Enhanced Datasets and a Bayesian Framework

Flood frequency analysis at large scales, essential for the development of flood risk maps, is hindered by the scarcity of gauge flow data. Suitable methods are thus required to predict flooding in ungauged basins, a notoriously complex problem in hydrology. We develop a Bayesian hierarchical model (BHM) based on the generalized extreme value (GEV) and the generalized Pareto distribution for regional flood frequency analysis at high resolution across a large part of North America. Our model leverages annual maximum flow data from ≈20,000 gauged stations and a dataset of 130 static catchment-specific covariates to predict extreme flows at all catchments over the continent as well as their associated statistical uncertainty. Additionally, a modification is made to the data layer of the BHM to include peaks over threshold flow data when available, which improves the precision of the discharge level estimates. We validated the model using a hold-out approach and found that its predictive power is very good for the GEV distribution location and scale parameters and improvable for the shape parameter, which is notoriously hard to estimate. The resulting discharge return levels yield a satisfying agreement when compared with the available design peak discharge from various government sources. The assessment of the covariates’ contributions to the model is also informative with regard to the most relevant underlying factors influencing flood-inducing peak flows. According to the developed aggregate importance score, the key covariates in our model are temperature-related bioindicators, the catchment drainage area and the geographical location.

Prediction of flash flood peak discharge in hilly areas with ungauged basins based on machine learning

ABSTRACT Peak discharge is an essential element of hydrological forecasting. Due to rapid outbreaks of flash floods in hilly areas and the lack of measured data, the fast and accurate estimation of peak discharge is crucial for flash flood hazard management. Three machine learning algorithms were applied to estimate peak discharge; this estimation was compared with the results of hydrological–hydraulic models, and the results were verified with measured watershed data. In this paper, 10 hydrological and geomorphological parameters were selected to predict the flood peak discharge in 103 watersheds in Taiyi Mountain North District. The results show that the particle swarm optimization backpropagation (PSO-BP) neural network model outperforms the BP neural network and random forest regression in prediction performance. PSO-BP has a lower mean absolute error (2.51%), root mean square error (3.74%), and mean absolute percentage error (2.74%) than the other models, which indicates that PSO-BP has high prediction accuracy. Importance analysis revealed that rainfall, early impact rainfall, catchment area, and rain intensity are the key input parameters of PSO-BP. The proposed method was confirmed to be a fast and relatively accurate algorithm for estimating the peak discharge of flash floods in ungauged basins.

Evaluation of hydrological models at gauged and ungauged basins using machine learning-based limits-of-acceptability and hydrological signatures

Hydrological models are evaluated by comparisons with observed hydrological quantities such as streamflow. A model evaluation procedure should account for dominantly epistemic errors in hydrological data such as model input precipitation and streamflow and avoid type-2 errors (rejecting a good model). This study uses quantile random forest (QRF) to develop limits-of-acceptability (LoA) over streamflows that account for uncertainties in precipitation and streamflow values. A significant advantage of this method is that it can be used to evaluate models even at ungauged basins. This method was used to evaluate a hydrological model –Sacramento Soil Moisture Accounting (SAC-SMA) – over the St. Joseph River Watershed (SJRW) for both gauged and hypothetical ungauged scenarios. QRF defined wide LoAs that yielded a large number of models as behavioral, suggesting the need for additional measures to develop a more discriminating inference procedure. The paper discusses why the LoAs defined by QRF were wide, along with some ways to define more discriminating LoAs. To further constrain the model, five streamflow-based signatures (i.e., autocorrelation function, Hurst exponent, baseflow index, flow duration curve, and long-term runoff coefficient) were used. The combination of LoAs over streamflow and streamflow-based signatures helped constrain the set of behavioral models in both the gauged and the ungauged scenarios. Among the signatures used in this study, the Hurst exponent and baseflow index were the most useful ones. All the 1-million models evaluated in this study were eventually rejected as unfit-for-purpose.

Assessing uncertainties of a remote sensing-based discharge reflectance model for applications to large rivers of the Congo Basin

ABSTRACT Adequate monitoring of river discharge is crucial for effective water resource management. However, this objective remains difficult to achieve in the context of large and ungauged river basins. This study assesses the performance of remote sensing applications for discharge monitoring in the lower reach of the Congo River, where daily discharge information is required to support many water resource operations. The approach is based on the use of Moderate Resolution Imaging Spectroradiometer (MODIS) remote sensing imagery to produce a daily time series of a ratio of reflectance values (C/M) for discharge monitoring. The validation of the approach is performed based on three-year water level data collected at the outlet gauging site and limited in situ Acoustic Doppler Current Profiler (ADCP) cross-section measurements for high and low flow seasons. The simulated discharge closely matches the observed values and falls within acceptable ranges, with errors below 10% and Nash-Sutcliffe coefficients ranging from 0.65 to 0.76 for ADCP and gauging station, respectively.

Hydroclimatic drivers of at‐a‐station hydraulic geometry of Brazilian rivers

The geometry of river channels defines their discharge conveyance capacity and can alter the flood regime. While most rivers follow the Theory of Hydraulic Geometry at-a-station power laws, the spatial patterns of exponents, coefficients, and hydrological controls beyond the scale of river reaches remain unclear. Here, we analyze 290 Brazilian river cross-sections to test the hypothesis that the hydraulic geometry is related to basin hydroclimatic characteristics, with a significant difference between humid and dry basins. We classify the basins draining to each cross-section according to the aridity index and the baseflow index, and analyze the controls on the exponents and coefficients (i.e., parameters) of the at-a-station hydraulic geometry power-laws. Our results show that the exponents and coefficients follow a spatially coherent pattern. The exponent of the width of AGH power law is related to the aridity of the basin, being larger in drier basins with a low baseflow index. The width and velocity coefficients are strongly dependent on the basin area. Whereas the velocity coefficient decreases in large basins, the width coefficient increases in large basins, and the spatial distribution of width exponent is related to the bankfull cross-section shape. The combined analysis of hydraulic geometry and basin characteristics is a promising tool for discharge estimation in ungauged basins, flood management, and maintenance of natural rivers.

Interpretable machine learning on large samples for supporting runoff estimation in ungauged basins

The distribution of flowmeter data and basin characteristic information exhibits substantial disparities, with most flow observations being recorded at a limited number of well-monitored locations. The perennial challenge of achieving reliable and robust hydrological modeling in ungauged catchments through regionalization has persisted. The increasing availability of large-scale hydrological datasets, coupled with recent advancements in machine learning techniques, offers new opportunities to explore patterns of association between basin attributes and hydrological parameters to enhance streamflow predictions. A novel parameter cross-regional transfer approach based on interpretable machine learning (XGBoost) is proposed to accurately predict runoff processes in ungauged regions by leveraging well-trained models across numerous basins within climate zones. We validate the effectiveness of this framework across 5,764 basins in a large sample dataset (Caravan), employing Nash-Sutcliffe Efficiency (NSE), RMSE and bias to assess performance. And a comparison is made with deep transfer learning based on LSTM and Transformer. Results indicate that the proposed method achieves NSE values exceeding 0.2 for 75 % of the ungauged basins, demonstrating superior performance and more stable accuracy compared to pure deep learning models, owing to its incorporation of physical constraints. Furthermore, the response of parameters to basin attributes within different climatic zones in the large-sample context is elucidated through SHAP values, enriching the understanding of hydrological features through data-driven inverse inference. These findings underscore the capability of interpretable machine learning to leverage hydro-physical regularities extracted from abundant basin features, thereby enhancing the accuracy of runoff predictions in ungauged regions.

A national-scale hybrid model for enhanced streamflow estimation – consolidating a physically based hydrological model with long short-term memory (LSTM) networks

Abstract. Accurate streamflow estimation is essential for effective water resource management and adapting to extreme events in the face of changing climate conditions. Hydrological models have been the conventional approach for streamflow interpolation and extrapolation in time and space for the past few decades. However, their large-scale applications have encountered challenges, including issues related to efficiency, complex parameterization, and constrained performance. Deep learning methods, such as long short-term memory (LSTM) networks, have emerged as a promising and efficient approach for large-scale streamflow estimation. In this study, we have conducted a series of experiments to identify optimal hybrid modeling schemes to consolidate physically based models with LSTM aimed at enhancing streamflow estimation in Denmark. The results show that the hybrid modeling schemes outperformed the Danish National Water Resources Model (DKM) in both gauged and ungauged basins. While the standalone LSTM rainfall–runoff model outperformed DKM in many basins, it faced challenges when predicting the streamflow in groundwater-dependent catchments. A serial hybrid modeling scheme (LSTM-q), which used DKM outputs and climate forcings as dynamic inputs for LSTM training, demonstrated higher performance. LSTM-q improved the mean Nash–Sutcliffe efficiency (NSE) by 0.22 in gauged basins and 0.12 in ungauged basins compared to DKM. Similar accuracy improvements were achieved with alternative hybrid schemes, i.e., by predicting the residuals between DKM-simulated streamflow and observations using LSTM. Moreover, the developed hybrid models enhanced the accuracy of extreme events, which encourages the integration of hybrid models within an operational forecasting framework. This study highlights the advantages of synergizing existing physically based hydrological models (PBMs) with LSTM models, and the proposed hybrid schemes hold the potential to achieve high-quality large-scale streamflow estimations.

Deciphering the Mechanism of Better Predictions of Regional LSTM Models in Ungauged Basins

AbstractPrediction in ungauged basins (PUB) is a concerning hydrological challenge, prompting the development of various regionalization methods to improve prediction accuracy. The long short‐term memory (LSTM) model has gained popularity in rainfall‐runoff prediction in recent years and has proven applicable in PUB. Prior research indicates that incorporating static attributes in the training of regional LSTM models could improve performance in PUB. However, the underlying reasons for this enhancement have received limited exploration. This study aims to explore the role of static attributes in the training of the regional LSTM model. It is assumed that the regional LSTM model can induce streamflow generation mechanisms with the incorporation of static attributes and apply certain streamflow generation mechanisms to ungauged catchments based on their attributes. To this end, a grouping‐based training strategy is proposed, that is, training and validating regional LSTM models on catchments with similar streamflow generation mechanisms within predefined groups. The training strategies of regional LSTM models, either incorporated with static catchment attributes or based on classification, are conducted in 363 catchments. Results demonstrate a high level of consistency in the enhancement achieved by the two training strategies. Specifically, 192 and 216 catchments exhibit enhancement compared to traditionally trained models without inclusion of attributes, with 132 catchments showing improvement under both training strategies. Furthermore, the findings indicate consistent spatial patterns and attribute distributions of enhanced catchments, as well as the notable improvement in reproducing low flow‐related hydrological signatures.

Understanding the association between global forest fire products and hydrometeorological variables

Climate change and anthropogenic activities have influenced the frequency and magnitude of forest fires both globally and regionally. While skilful short- to extended-range prediction of forest fires is essential for effective mitigation in local communities, it is also important to identify the implications of forest fires on different sectors, including water resources and sustainable development. Limited studies have investigated the association between forest fires and hydrometeorological variables at the regional scale in developing countries due to the lack of necessary datasets, which can now be leveraged using the newly hosted global reanalysis of fire danger indices (referred to as fire indices). The current study presents a comprehensive analysis of the spatio-temporal variations of eight fire indices across India, as well as their association with hydro-meteorological variables, such as precipitation, temperature, and the streamflow of a major river basin (Mahanadi) in India. The accuracy of these indices in capturing real fire events and the potential benefit of incorporating fire indices into long-term hydrologic simulations are also explored. The results show that fire indices can accurately yield fire seasons (i.e., post-monsoon and summer) in India. Furthermore, forest fires are found to be strongly associated with hydro-meteorological variables, typically resulting in low streamflow regimes. Fire indices can also capture actual fire events, maintaining high scalar accuracy. Finally, an improvement in uncalibrated hydrologic model simulations is observed when simulated streamflow is post-processed using the fire indices as predictors. Overall, the current study has valuable implications for fire indices forecasting and hydrologic simulations in ungauged basins.

Toward Flash Flood Modeling Using Gradient Resolving Representative Hillslopes

AbstractIt is increasingly acknowledged that the acceleration of the global water cycle, largely driven by anthropogenic climate change, has a disproportionate impact on sub‐daily and small‐scale hydrological extreme events such as flash floods. These events occur thereby at local scales within minutes to hours, typically in response to high‐intensity rainfall events associated with convective storms. In the present work, we show that by employing physically based representative hillslope models that resolve the main gradients controlling overland flow hydrology and hydraulics, we can get reliable simulations of flash flood response in small data‐scarce catchments. To this end, we use climate reanalysis products and transfer soil parameters previously obtained for hydrological predictions in an experimental catchment in the same landscape. The inverted mass balance of flood reservoirs downstream is employed for model evaluation in these nearly ungauged basins. We show that our approach using representative hillslopes and climate data sets can provide reasonable uncalibrated estimates of the overland runoff response (flood magnitude, storm volume, and event runoff coefficients) in three of the four catchments considered. Given that flash floods typically occur at scales of a few km2 and in ungauged places, our results have implications for operational flash flood forecasting and open new avenues for using gradient resolving physically based models for the design of small and medium flood retention basins around the world.

Modularity Density Based-Edge Betweenness Method for Catchment Classification in Sabah

One of the most significant studies in hydrology is the classification of catchments. There are various reasons why catchment classification studies have been conducted, but most importantly is to anticipate the prediction of ungauged basin (PUB), among others. There are several ways of classification, each with its own set of assumptions and basis, that have been used for catchment classification. Recently, complex network concepts, specifically community structure, have emerged as key classification methods, and are now attaining traction in catchment classification. As a result, in this study, community structure approach, specifically the Modularity Density based- Edge Betweenness (MDEB) method is implemented to split the network into communities. By using the proposed method, a network of 30 monthly streamflow stations across Sabah is considered for catchment classification. The impact of correlation threshold, which vary from 0 to 1, denoting the strength between a pair of catchments is also investigated. As a result, four threshold values are chosen (T = 0.5, 0.55, 0.6, and 0.65), and the communities established with threshold value, T=0.6 are interpreted. The relationship between catchment characteristics and flow characteristics are investigated as well as distance-correlation relationships for communities identified to understand the Sabah catchment’s behaviors.