Application Of Hydrological Models Research Articles

Opportunities and challenges for precipitation forcing data in post‐wildfire hydrologic modeling applications

AbstractThe frequency and extent of wildfires have increased in recent decades with immediate and cascading effects on water availability in many regions of the world. Precipitation is used as primary input to hydrologic models and is a critical driver of post‐wildfire hydrologic hazards including debris flows, flash floods, water‐quality effects, and reservoir sedimentation. These models are valuable tools for understanding the hydrologic response to wildfire but require accurate precipitation data at suitable spatial and temporal resolutions. Wildfires often occur in data‐sparse, headwater catchments in complex terrain, and post‐wildfire hydrologic effects are particularly sensitive to high‐intensity, short‐duration precipitation events, which are highly variable and difficult to measure or estimate. Therefore, the assessment and prediction of wildfire‐induced changes to watershed hydrology, including the associated effects on ecosystems and communities, are complicated by uncertainty in precipitation data. When direct measurements of precipitation are not available, datasets of indirect measurements or estimates are often used. Choosing the most appropriate precipitation dataset can be difficult as different datasets have unique trade‐offs in terms of spatial and temporal accuracy, resolution, and completeness. Here, we outline the challenges and opportunities associated with different precipitation datasets as they apply to post‐wildfire hydrologic models and modeling objectives. We highlight the need for expanded precipitation gage deployment in wildfire‐prone areas and discuss potential opportunities for future research and the integration of precipitation data from disparate sources into a common hydrologic modeling framework.This article is categorized under: Science of Water > Hydrological Processes Science of Water > Methods Science of Water > Water and Environmental Change

Strategic flood management using collaborative game theory: conjunct application of hydrologic and strategic planning models

Strategic flood management using collaborative game theory: conjunct application of hydrologic and strategic planning models

Improving Flash Flood Hydrodynamic Simulations by Integrating Leaf Litter and Interception Processes in Steep-Sloped Natural Watersheds

More frequent high-intensity, short-duration rainfall events increase the risk of flash floods on steeply sloped watersheds. Where measured data are unavailable, numerical models emerge as valuable tools for predicting flash floods. Recent applications of various hydrological and hydrodynamic models to predict overland flow have highlighted the need for improved representations of the complex flow processes that are inherent in flash floods. This study aimed to identify an optimal modeling approach for characterizing leaf litter losses during flash floods. At a gauged watershed in the Hidegvíz Valley in Hungary, a physical-based model was calibrated using two distinct rainfall–runoff events. Two modeling methodologies were implemented, integrating canopy interception and leaf litter storage, to understand their contributions during flash flood events. The results from the model’s calibration demonstrated this approach’s effectiveness in determining the impact of leaf litter on steep-sloped watersheds. Soil parameters can estimate the behavior of leaf litter during flash flood events. In this study, hydraulic conductivity and initial water content emerged as critical factors for effective parametrization. The findings underscore the potential of a hydrodynamic model to explore the relationship between leaf litter and flash flood events, providing a framework for future studies in watershed management and risk-mitigation strategies.

Improving probabilistic streamflow predictions through a nonparametric residual error model

Reliable probabilistic hydrological prediction requires appropriate handling of residual errors, which can pose considerable complexity. This paper proposes a nonparametric residual error (NRE) model that effectively captures the statistical characteristics of raw residuals. The NRE model employs a local linear estimator with a robust bandwidth selector to estimate the regression and conditional volatility functions of raw residuals. Additionally, the AR(1) model and location-mixture Gaussian distribution are used to estimate the temporal correlation structure and innovation distribution. Through two case studies in South-East China, this research demonstrates the superiority of the NRE model over the benchmark Box-Cox transformation approach in terms of prediction reliability, precision, and bias correction capabilities. Simulation experiments further reveal that the NRE model can effectively fit the residual regression function, conditional volatility function, and innovation distribution under varying scenarios. The proposed residual error model is anticipated to promote the adoption of probabilistic predictions in hydrological modeling applications.

Wavelet analysis of rainfall and application of hydrological model in a semi‐arid river basin of Rajasthan, India

AbstractRainfall–runoff modeling requires a selection of a suitable hydrologic model for the determination of an accurate quantity. A better understanding of rainfall–runoff processes is important for stream flow generation within a river basin. This study investigate the long‐term spatial–temporal trend of rainfall and streamflow using the hydrologic engineering center‐hydrologic modeling system (HEC‐HMS) model to understand the rainfall–runoff dynamics in the Banas River basin. Rainfall variability was analyzed for two periods: 1971–1995 and 1996–2020. Continuous wavelet transform was used to analyze the periodicity and the inter‐seasonal relationship in rainfall. HEC‐GeoHMS was used to generate the various inputs for the hydrologic modeling inputs for the HEC‐HMS model. Initial constant loss and ModClark transform method were used to set up the HEC‐HMS model for the study area. Calibration and validation of the model were performed using 3 years of observed data 2010–2012 and 2013–2015, respectively. Statistical model efficiency was checked using the coefficient of determination (R2), Nash–Sutcliffe efficiency, and root mean squared error to evaluate the performance of the HEC‐HMS model. The finding indicated that the R2 values are 0.80 for calibration and 0.82 for validation periods, respectively, which are in good arrangement with the measured values. This study analyzes the relationship between rainfall and runoff, which will aid in proper and efficient water resource management.

Improving rainfall-runoff modeling in the Mekong river basin using bias-corrected satellite precipitation products by convolutional neural networks

Accurate rainfall-runoff (RR) modeling is crucial for effective Mekong River Basin (MRB) water resource management. Satellite precipitation products (SPPs) can offer valuable data for such modeling; however, these products often exhibit biases that may adversely affect hydrological simulations. This study aimed to improve RR modeling using bias-corrected SPPs and the Soil and Water Assessment Tool (SWAT) model for MRB. A convolutional neural network-based deep learning framework was employed to correct biases in four SPPs (TRMM, PERSIANN-CDR, CHIRPS, and CMORPH), resulting in four respective bias-corrected SPPs (ADJ_TRMM, ADJ_CDR, ADJ_CHIR, and ADJ_CMOR). The bias-corrected products were compared against a gauge-based dataset in terms of rainfall analysis, and their performance within the SWAT model was assessed over calibration (2004–2013) and validation (2014–2015). Bias-corrected products demonstrated superior performance in rainfall analysis, with ADJ_TRMM outperforming other products. The SWAT model calibration results showed satisfactory performance across all stations, with a Nash-Sutcliffe Efficiency (NSE) ranging from [0.76–0.87]. Integrating bias-corrected SPPs into the SWAT model significantly increased the RR simulations in the MRB, indicated by higher NSE values [0.72–0.85] compared to uncorrected SPPs [-0.37 to 0.85] at the Kratie station. Besides, the inconsistent performance of bias-corrected products between rainfall analysis and RR modeling was observed, with ADJ_CDR outperforming ADJ_TRMM in the SWAT model. These results highlight the significance of using bias-corrected SPPs in hydrological modeling applications, especially in areas with limited ground-based precipitation data, and highlight the need for further research to refine bias correction methods and address the limitations of the SWAT model.

Application of a GIS-Based Hydrological Model to Predict Surface Wetness of Blanket Bogs

Understanding hydrological processes operating on relatively intact blanket bogs provides a scientific basis for establishing achievable restoration targets for damaged sites. A GIS-based hydrological model, developed to assess restoration potential of Irish raised bogs, was adapted and applied to four relatively intact blanket bogs in Ireland. The Modified Flow Accumulation Capacity (MFAC) model utilised high-resolution topographic data to predict surface wetness, based on climatic conditions, contributing catchment and local surface slope. Modifications to MFAC parameters aimed to account for differences in hydrological processes between raised bogs and blanket bogs. Application of a climatic correction factor accounted for variations in effective rainfall between the four study sites, while monitoring of water table levels indicated a log-linear relationship between MFAC values and summer water table levels and range of water table fluctuations. Deviations from the observed relationship between MFAC and water table levels were associated with hydrological pressures, such as artificial drainage or the occurrence of subsurface macropores (peat pipes), which further lowered summer water tables. Despite being effective as a predictor of relative surface wetness, the relationship between MFAC and ecological variables such as Sphagnum spp. cover proved poor, pointing to the impact of past activities and damage caused by anthropogenic pressures. Findings demonstrated MFAC as an effective tool in predicting surface wetness within blanket bog-covered landscapes, thus proving useful to peatland practitioners in planning and prioritising areas for restoration.

Combining large-scale and regional hydrological forecasts using simple methods

The development and expanded application of large-scale hydrological models has produced forecasts that often overlap with more targeted, regional hydrological forecasts. Here the possibility is explored for using simple methods to combine forecasts from a large-scale model, the Great Lakes portion of the National Surface and River Prediction System (NSRPS), and a regional system, the Système de Prévision Hydrologique (SPH) which covers southern Quebec, to improve regional forecasts. Outputs from the two forecasting systems are combined using multiple methods, including the simple mean, a weighted average in which the weights are optimized using the Kling-Gupta Efficiency (KGE), the Reduced Continuous Ranked Probability Score (RCRPS), and Ignorance Score (IGN) as cost functions, and weights calculated from the residual errors of the models. Bayesian Model Averaging (BMA) is also used to combine the probabilistic forecasts from both systems. The results show that it is possible to improve regional hydrological forecasts by using simple weighted combinations with forecasts from the large-scale system, even though the regional system performs clearly better. Performance is assessed via many well-known metrics, such as Nash-Sutcliffe Efficiency (NSE), KGE, RCRPS, and IGN. Results are averaged over 40 gauging stations and analyzed at lead times from 3 to 120 h. Improvements in all criteria for lead times over 60 h are observed, and there is no loss in performance at any lead times. Finally, the methods are used in a leave-one-out setup containing 29 validation basins to simulate performance on ungauged basins. The performance gain for ungauged basins is similar to that of the gauged basins, demonstrating that these simple methods can also improve forecasts in more remote territories where no gauging is available.

Spatial Downscaling of Soil Moisture Based on Fusion Methods in Complex Terrains

Large-area soil moisture (SM) data with high resolution and precision are the foundation for the research and application of hydrological and meteorological models, water resource evaluation, agricultural management, and warning of geological disasters. It is still challenging to downscale SM products in complex terrains that require fine spatial details. In this study, SM data from the Soil Moisture Active and Passive (SMAP) satellite were downscaled from 36 to 1 km in the summer and autumn of 2017 in Sichuan Province, China. Genetic-algorithm-optimized backpropagation (GABP) neural network, random forest, and convolutional neural network were applied. A fusion model between SM and longitude, latitude, elevation, slope, aspect, land-cover type, land surface temperature, normalized difference vegetation index, enhanced vegetation index, evapotranspiration, day sequence, and AM/PM was established. After downscaling, the in situ information was fused through a geographical analysis combined with a spatial interpolation to improve the quality of the downscaled SM. The comparative results show that in complex terrains, the GABP neural network better captures the soil moisture variations in both time and space domains. The GDA_Kriging method is able to merge in situ information in the downscaled SM while simultaneously maintaining the dynamic range and spatial details.

Application of hydrological models in climate change framework for a river basin in India

Abstract Soil Water Assessment Tool (SWAT), Hydrologic Engineering Center-Hydrologic Modelling System (HEC-HMS), and Hydrologic Simulation Program Fortran (HSPF) are explored for streamflow simulation of Lower Godavari Basin, India. The simulating ability of models is evaluated using four indicators. SWAT has shown exceptional simulating ability in calibration and validation compared to the other two. Accordingly, SWAT is used in the climate change framework using an ensemble of 13 Global Climate Models and four Shared Socioeconomic Pathways (SSPs). Three time segments, near-future (2021–2046), mid-future (2047–2072), and far-future (2073–2099), are considered for analysis. Four SSPs show a substantial increase in streamflow compared to the historical period (1982–2020). These deviations range from 17.14 (in SSP245) to 28.35% (in SSP126) (near-future), 31.32 (SSP370) to 43.28% (SSP585) (mid-future), and 30.41 (SSP126) to 70.8% (SSP585) (far-future). Across all timescales covering 948 months, the highest projected streamflows observed in SSP126, SSP245, SSP370, and SSP585 were 4962.36, 6,108, 6,821, and 6,845 m3/s, respectively. Efforts are also made to appraise the influence of multi-model combinations on streamflow. The present study is expected to provide a platform for holistic decision-making, which helps develop efficient basin planning and management alternatives.

Application of geospatial technique and hydrological modelling in evaluating reservoir sedimentation in Chhattisgarh state of India

ABSTRACT Soil erosion and sediment deposition in the reservoirs are both consequences of the change in precipitation pattern that has been observed over the past decade. For the purpose of storing water in reservoirs built before the 1980s, the Indian state of Chhattisgarh has a few irrigation projects. As the state has grown, agricultural land has increased while the proportion of forest area has decreased. Due to this development, runoff is introducing more sediment into the reservoirs, resulting in a reduction in intended storage. The effects of sediment deposition in the Kodar reservoir, located in the Mahasamund district of Chhattisgarh State, are evaluated in present study. Prior to five years, the reservoir reported a 30% deficit in actual irrigation. To quantify the sediment load, Remote Sensing (RS), Geographic Information System (GIS), and the Hydrologic Engineering Centre – Hydrologic Modelling System (HEC-HMS) were effectively employed. From 2012 to 2014, the reservoir received an average sediment load of 1.11 million tonnes. In order to preserve soil in the watershed’s erosion-prone area, a treatment plan is developed. It includes numerous thematic maps that are overlaid in order to provide preventive measures against reservoir sedimentation and is further proposed in the form of best management practices.

DSOLMap, a novel high-resolution global digital soil property map for the SWAT + model: Development and hydrological evaluation

This research paper addresses the ongoing challenge of developing fine-resolution global digital soil property maps for hydrological modelling applications. Hydrological models are essential for understanding watershed dynamics and the impact of human activities on water resources. Soil data, which plays a crucial role in the hydrological cycle, is a requisite model input. Global digital soil property maps usually have coarse spatial resolutions, adding considerable uncertainty to hydrological models despite calibration efforts. To address this issue, a new global digital soil property map with 250 m spatial resolution, known as Digital Soil Open Land Map (DSOLMap), was developed and evaluated in this study. The DSOLMap has a finer spatial resolution than existing global soil maps and a more detailed soil profile divided into six soil horizons. This new high-resolution global digital soil property map was tailored to the SWAT + model format. SWAT + is the latest released version of the Soil and Water Assessment Tool (SWAT), one of the most comprehensive hydrological models, and is widely used worldwide. A hydrological evaluation was conducted with the DSOLMap and its results were compared to two other global soil databases using the SWAT + model in a basin located in the north of Spain. The findings showed that using more detailed, finer-resolution soil data, such as those that the DSOLMap offers, improved the hydrological performance of the SWAT + model on a daily scale before and after calibration and validation procedures. The DSOLMap represents a global step forward in hydrological modelling, notably for regions with scarce or unavailable soil information. This new digital soil property map can help decision-makers address global challenges related to water resources and environmental issues through hydrological modelling.

Ensemble Estimation of Historical Evapotranspiration for the Conterminous U.S.

AbstractEvapotranspiration (ET) is the largest component of the water budget, accounting for the majority of the water available from precipitation. ET is challenging to quantify because of the uncertainties associated with the many ET equations currently in use, and because observations of ET are uncertain and sparse. In this study, we combine information provided by available ET data and equations to produce a new monthly data set for ET for the conterminous U.S. (CONUS). These maps are produced from 1895 to 2018 at an 800 m spatial scale, marking a finer resolution than currently available products over this time period. In our approach, the relative performance of a suite of ET equations is assessed using water balance, flux tower, and remotely sensed ET estimates. At the observation locations, we use error distributions to quantify relative weights for the equations and use these in a modified Bayesian model averaging weighted ensemble approach. The relative weights are spatially generalized using a random forest regression, which is applied to wall‐to‐wall explanatory variable maps to generate CONUS‐wide relative weight maps and ensemble estimates. We assess the performance of the ensemble using a reserved subset of the observations and compare this performance against other national‐scale map products for historical to modern ET. The ensemble ET maps are shown to provide an improved accuracy over the alternative comparison products. These ET maps could be useful for a variety of hydrologic modeling and assessment applications that benefit from a long record, such as the study of periods of water scarcity through time.

Application of hydrological model to simulate streamflow contribution on water balance in Himalaya river basin, Nepal

Hydrological models are widely used and often regarded as reliable tools for accurately estimating various components of the water balance. In a remote Himalayan catchment, such as Tamakoshi basin, where limited hydrometric dataset is available, such models often provide essential insights that are crucial to water researchers and planners. In this regard, we employed the semi-distributed HBV-light (version 4.0.0.25) hydrological model for glacierized Tamakoshi river basin and attempted to quantify various water balance components. For our model tests, using the daily streamflow records, we selected two distinct periods, i.e., 2004–2008 as a calibration period whilst 2011–2012 for model validation. Based on our findings, the model was able to reasonably predict the streamflow (validation efficiency: Nash-Sutcliffe Efficiency of 0.82 and percent bias −21%). At our site, HBV-light model predicted that the change in streamflow was mostly governed by monsoonal rain (62%) followed by baseflow (20%), glacier melt (13%) and snowmelt (5%). As expected, the streamflow peaked during the month of August where monsoon-induced rain and melting of glaciers significantly contributed to river flow. As a result, monsoon period showcased largest fluctuation in water storage while negligible change was observed during post-monsoon season. Nonetheless, our findings revealed that the baseflow contribution to streamflow was maximum during the month of October and lowest during February. Our findings indicated that the water balance of the Tamakoshi basin is largely influenced by monsoonal rain during June–September window as well as baseflow and glacier melt during the dry season. Runoff components contribution to streamflow was increasing but water storage changes was decreasing in recent decade (2011–2020). We believe our findings are crucial for future initiatives involving water resources, water-induced disaster management, and studies of climate change may benefit from the findings of this study, especially in a region with limited hydrometric data availability.

Fields of Application of SWAT Hydrological Model—A Review

Soil and Water Assessment Tool (SWAT) is a widely used model for runoff, non-point source pollution, and other complex hydrological processes under changing environments (groundwater flow, evapotranspiration, snow melting, etc.). This paper reviews the key characteristics and applications of SWAT. Since its inception in the 1990s, there has been a significant increase in the number of articles related to the SWAT model. In the last 10 years, the number of articles almost reached 4000. The range of applications varies between small and large scales; however, large watershed modelling dominates in North America and Asia. Moreover, the prevailing modelling is related to hydrological impacts in a changing environment, which is a global problem. The significant shortcoming of the SWAT model is the vast quantity of data necessary to run the model to generate accurate and reliable results, which is not accessible in some regions of the world. Apart from its accessibility, it has several advantages, including continuous development, which results in a slew of new interfaces and tools supporting the model. Additionally, it can simulate human activity and agricultural measures and adapt to new circumstances and situations. This article emphasizes weaknesses and strengths of SWAT model application on modelling of hydrological processes in changing climate and environment.

Temporal Transferability of Calibrated Hydrological Model Parameters: A Case Study of Gin Catchment, Sri Lanka

Lack of observed data for model calibration hinders the application of hydrological models in many poorly-gauged catchments, particularly in the humid tropical region. Despite much less attention given, it is vital to assess transferability of calibrated parameters in order to apply hydrological models in such catchments to assist their water resources planning and management activities. Thus, this study investigated temporal transferability of a lumped conceptual hydrological model’s (MIKE 11 NAM) calibrated model parameters for rainfall-runoff simulations in two different time periods beyond its calibration period. Study area was selected as Gin catchment located in the humid tropical region. MIKE 11 NAM model was calibrated for the period 1995-1998 [Nash Sutcliffe coefficient (NSE) = 0.73, percent bias (PBIAS) = 3.9%, ratio of the root mean square error to the standard deviation of measured data (RSR) = 0.52] and validated for the period 1999-2002 (NSE=0.66, PBIAS = 8.7%, RSR = 0.59) for the Gin catchment. The temporal transferability of the calibrated model parameters was tested using two scenarios which formulated based on the temporal lag between the calibration period and the transfer period: scenario A having a 4-year time lag and scenario B having a 8-year time lag. Scenario A which evaluated the model performance using 2003-2006 streamflow data indicated only a marginal loss in the model performance in comparison to the calibration. It showed an overall ‘good’ performance (NSE=0.64, PBIAS = 8.6%, RSR = 0.59) including promising capability to reproduce the peak flows (<10th percentile) with Pearson’s correlation coefficient of 0.6. However, scenario B which evaluated the model performance using 2007-2010 streamflow data indicated ‘unsatisfactory’ model performance (NSE=0.42, PBIAS = 13.6%, RSR = 0.76). Therefore, this study suggests that the calibrated parameters of MIKE 11 NAM model can be temporally transferred within a catchment with a 4-year time lag from the calibration period implying the applicability of this modelling framework for rainfall-runoff simulations especially in the catchments where streamflow data is sparse.

Application of SWAT Hydrological Model to Simulate Flow of Seti-Gandaki Basin

The Soil and Water Assessment Tool (SWAT), a semi-distributed hydrological model, has been used for the Seti-Gandaki River Basin (SGRB) to simulate streamflow. Further, SWAT was assessed to study water balance of the basin. This study primarily focuses on different features of hydrological modeling like multi-site calibration and validation of the streamflow with a view to check the reliability of the model in the high precipitation basin of Nepal. The statistics performance of the model is evaluated using various statistical test like Nash-Sutcliffe, RMSE, and Kling Gupta Efficiency (KGE). Moreover, the study used various statistical parameters like Nash-Sutcliffe efficiency (NSE), Percentage Bias (PBIAS), Coefficient of Variation (R2), ratio of the root mean square error to the standard deviation of measures data (RSR), and Kling Gupta Efficiency (KGE) to evaluate the performance of the model and carryout0.93(0.89), -0.04(-21.87), 0.95(0.94), 0.26(0.34), and 0.85(0.76), respectively, for calibration (and validation) at Damauli station. The mean annual flow and annual precipitation at SGRB was observed to be 209 m3/s and 2866 mm, respectively. About 20% of annual precipitation seems to be lost as evapotranspiration. The statistics results showed that the model performed better for daily and monthly periods. Overall, the versatility and reliability of SWAT is an appropriate hydrological modeling tool for water resources over the study region. The output of the study can be helpful for the planning and management of water resources in high precipitation basins.

Combined Impacts of Climate Change and Water Withdrawals on the Water Balance at the Watershed Scale—The Case of the Allier Alluvial Hydrosystem (France)

The Allier River and its alluvial aquifer constitute a shallow but highly productive water resource due to their hydrodynamic properties. This hydrosystem provides almost all of the water requirements for domestic supply and irrigation. Recent dry summers (such as those in 2015, 2019, and 2022) and the lack of winter recharge have led managers to question the sustainability of this resource. We proposed the use of hydrological modelling with Gardenia with which the water balance can be determined at the watershed scale (7020 km2) and with which forecasting simulations can be performed for 2030–2070. Thus, this work was divided into (1) model calibration (2000–2020), (2) the determination of the main drivers of the water balance (2000–2020), (3) and river flow and groundwater level simulation (2030–2070). For the latter, Gardenia was used considering a “better case”, using the RCM Aladin63 in RCP2.6, and considering a “worst case”, using the RCM RegCM4-6 in RCP8.5. The calibration for 2000–2014 showed good reproducibility of river flows (NSE = 0.91) and groundwater levels (NSE = 0.85). The model showed that the major drivers in 2000–2020 were actual evapotranspiration and effective precipitation, which, respectively, represented 68% and 32% of mean annual precipitation. Water withdrawals did not significantly contribute to the water balance with the exception of those in very dry summers, such as those in 2003, 2005, 2015, and 2019. Climate appeared, therefore, as a prevalent factor of the Allier hydrosystem functioning compared to global withdrawals except for that during these dry years. Prospective simulations showed a decline in annual river flows and groundwater levels by a maximum of −15% and −0.08 m asl (“worst case”), respectively. These simulations showed that the Allier hydrosystem will be able to meet the water needs for various uses until 2070. In detail, it is likely that summer shortages will no longer be compensated by the Naussac Dam if the hydrosystem faces more than two years of drought. In this case, water-saving solutions will have to be found. This study is, thus, a good example of the application of hydrological modelling to address management issues in such a hydrosystem.

A Hydrological Data Prediction Model Based on LSTM with Attention Mechanism

With the rapid development of IoT, big data and artificial intelligence, the research and application of data-driven hydrological models are increasing. However, when conducting time series analysis, many prediction models are often directly based on the following assumptions: hydrologic time series are normal, homogeneous, smooth and non-trending, which are not always all true. To address the related issues, a solution for short-term hydrological forecasting is proposed. Firstly, a feature test is conducted to verify whether the hydrological time series are normal, homogeneous, smooth and non-trending; secondly, a sequence-to-sequence (seq2seq)-based short-term water level prediction model (LSTM-seq2seq) is proposed to improve the accuracy of hydrological prediction. The model uses a long short-term memory neural network (LSTM) as an encoding layer to encode the historical flow sequence into a context vector, and another LSTM as a decoding layer to decode the context vector in order to predict the target runoff, by superimposing on the attention mechanism, aiming at improving the prediction accuracy. Using the experimental data regarding the water level of the Chu River, the model is compared to other models based on the analysis of normality, smoothness, homogeneity and trending of different water level data. The results show that the prediction accuracy of the proposed model is greater than that of the data set without these characteristics for the data set with normality, smoothness, homogeneity and trend. Flow data at Runcheng, Wuzhi, Baima Temple, Longmen Town, Dongwan, Lu’s and Tongguan are used as input data sets to train and evaluate the model. Metrics RMSE and NSE are used to evaluate the prediction accuracy and convergence speed of the model. The results show that the prediction accuracy of LSTM-seq2seq and LSTM-BP models is higher than other models. Furthermore, the convergence process of the LSTM-seq2seq model is the fastest among the compared models.

Sediment load estimation using a novel regionalization sediment-response similarity method for ungauged catchments

The development and application of reliable hydrological models are challenging in ungauged or poorly gauged catchments. Although several regionalization methods have been developed for predicting hydrological variables in ungauged catchments, a large uncertainty usually exists in the prediction because previous methods typically depended on several static catchment attributes or long-term mean climatic descriptors, thereby neglecting the spatiotemporal variability of the climate. In this study, we propose a new regionalization method called sediment-response similarity (SRS) using the soil and water assessment tool model and self-organizing maps to estimate sediment load in ungauged catchments, considering spatiotemporal variability and the sediment load relation to rainfall characteristics of individual catchments. The performance of SRS was evaluated by comparing it against the conventional regionalization method (i.e., physical similarity) and calibration model results, in four gauged catchments of the lower Mekong River basin. The SRS method showed improved performance over the conventional approach in estimating sediment loads, with an average Nash-Sutcliffe efficiency (NSE) and coefficient of determination (R2) of 0.75 and 0.76, respectively, which were close to the respective values of NSE and R2 = 0.77, of the calibration model. Additionally, the SRS approach showed an error reduction of up to 7 %, compared with that of the physical similarity method, indicating the potential of the proposed regionalization method in estimating catchment-wide sediment load in ungauged catchments. The findings indicate that the SRS method ideally determines the similarity of sediment loads between gauged and ungauged catchments. The SRS approach effectively tackled the main challenge of selecting catchment attributes in the conventional regionalization method and minimizing the uncertainty of sediment prediction in ungauged catchments. This novel SRS method is a promising method for estimating, not only sediment load, but also other hydrological variables and rainfall-driven phenomena in the ungauged catchments globally.