Streamflow Values Research Articles

Hybrid physically based and machine learning model to enhance high streamflow prediction

ABSTRACT Despite the significant performance of machine learning models for streamflow prediction, their precision for poorly represented data is reduced. This is a concern for flood mitigation purposes where high streamflow values are the most relevant but scarce. Consequently, this study proposes a methodology to create a hybrid model to mitigate the accuracy reduction of a standalone machine learning model in high streamflow values. The hybrid model combines a surrogate model that reproduces a physically based model with a model to estimate its residuals employing the Random Forest algorithm. The hybrid model reaches a root mean squared error reduction of 23% and 33% in the study catchments for values over a three-year return period compared to a standalone machine learning model. The percentage bias decreases by more than 70% from values over a 1.5-year return period. Moreover, the hybrid model has shown close predictions of values higher than the training set.

Regionalization of Climate Elasticity Preserves Dooge's Complementary Relationship

AbstractClimate elasticity of streamflow represents a nondimensional measure of the sensitivity of streamflow to climatic factors. Estimation of such elasticities from observational records has become an important alternative to scenario‐based methods of evaluating streamflow sensitivity to climate. Nearly all previous elasticity studies have used a definition of elasticity known as arc elasticity, which measures changes in streamflow about mean values of streamflow and climate. Using observational records in the western U.S., our findings reveal that elasticity definitions based on power law models lead to both regional and basin specific estimates of elasticity which are physically more realistic than estimates based on arc elasticity. Evaluating the ability of arc and power law elasticity estimators in reproducing Dooge's complementary relationship (DCR) between potential evapotranspiration and precipitation elasticities reveal that power law elasticities estimated from at‐site, panel, and hierarchical statistical models reproduce DCR, whereas corresponding estimators based on arc elasticity cannot reproduce DCR. Importantly, our regional elasticity formulations using panel and hierarchical formulations led to estimates of both regional and basin specific estimates of elasticities, enabling and contrasting streamflow sensitivity to climate across both basins and regions.

Hyperparameter optimization of regional hydrological LSTMs by random search: A case study from Basque Country, Spain

This paper introduces a novel approach for hyperparameter optimization of long short-term memory networks (LSTMs) to achieve highly accurate hourly streamflow and water level predictions in the realm of regional rainfall-runoff modeling. Leveraging simultaneous systematic hyperparameter optimization of 10 distinct hyperparameters by Random Search, the study achieves high accuracy in terms of predictions across 40 humid flashy catchments in Basque Country, north of Spain. By carefully designing the search space and incorporating domain expertise, the approach quickly converges to optimal and highly accurate network configurations with both efficiency and efficacy. LSTMs ingested precipitation, temperature, and potential evapotranspiration as inputs to predict 2 targets of streamflow and water level, in an hourly timestep. On the test set, the optimized LSTM networks accurately predicted streamflow and water level with Nash-Sutcliffe (NSE) and Kling-Gupta (KGE) efficiencies as high as 0.97, in one of the catchments. Across all 40 studied catchments, the overall average NSE and KGE values for streamflow were 0.89 and 0.87, respectively; water level exhibited average NSE and KGE scores of 0.91 and 0.92.Moreover, statistical analysis reveals significant differences in the performance of the 2 distinct optimized network architectures in different hydrological catchments, underscoring the importance of deliberate network configuration selection post-random search. This selection process is vital for achieving higher performance in as many catchments as possible. The findings highlight opportunities for enhancing the “learning maturity” of regional hydrological deep learning LSTM networks. This research provides valuable insights for researchers and practitioners involved in optimizing regional hydrological deep learning models for a variety of applications and on new datasets.

Streamflow trends and flood frequency analysis: a regional study of the UK.

In recent years, the escalating effects of climate change on surface water bodies have underscored the critical importance of analyzing streamflow trends for effective water resource planning and management. This study conducts a comprehensive regional investigation into the streamflow rate trends of 18 rivers across the United Kingdom (UK). An enhanced Mann-Kendall (MK) test was employed to meticulously analyze both rainfall and streamflow trends on monthly and annual scales. Additionally, the Innovative Trend Analysis (ITA) method was applied to elucidate the variability of streamflow rates, providing a more nuanced understanding of hydrological changes in response to climatic shifts. MK test reveals statistically significant positive trends in streamflow rates, particularly for rivers in south-central Scotland and northern England. Specifically, in January, rivers such as the Tay at Ballathie, Tweed at Peebles, and Teviot at Ormiston showed Z-scores above 2. Annually, similar positive trends were observed, with the Tay at Ballathie (Z = 3.42) and Nith at Friars Carse (Z = 3.35) exhibiting the highest increases in streamflow rates. The ITA method showed no relevant trends for the lowest values of streamflow, except for the Thames at Kingston, while considerable variability was observed for the highest streamflow rates, with several rivers showing positive trends and, however, some England rivers, like Bure at Ingworth, Test at Broadlands, and Trent at Colwick, showing negative trends. From this perspective, a more in-depth analysis of the extreme streamflow trends was carried out. In particular, the flood frequency of the maximum annual streamflow was assessed, based on the fitting of the Generalized Extreme Value (GEV) distribution on the annual maxima. Increasing location parameter (μ) and return period trends were observed for several rivers across the UK. In particular, the Tay at Ballathie (Scotland) showed the most marked increase, with μ that ranged from about 730 m3/s to more than 900 m3/s. At the same time, slight decreasing trends were observed for the Trent River (μ from 378 m3/s to 341 m3/s). The critical comparison of the MK test, ITA, and GEV distribution fitting revealed both agreements and discrepancies among the methods. While the analyses generally aligned in detecting significant trends in streamflow rates, notable discrepancies were observed, particularly in rivers with negligible trends. These inconsistencies highlight the complexity of hydrological responses and the limitations of individual methods. Overall, the study provides a comprehensive view of how streamflow dynamics are evolving in UK rivers, highlighting regional variations in the impact of climate change. This understanding can improve water resource management strategies by integrating diverse analytical approaches.

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.

Sediment load forecasting from a biomimetic optimization perspective: Firefly and Artificial Bee Colony algorithms empowered neural network modeling in Çoruh River

The service life of downstream dams, river hydraulics, waterworks construction, and reservoir management is significantly affected by the amount of sediment load (SL). This study combined models such as the artificial neural network (ANN) algorithm with the Firefly algorithm (FA) and Artificial Bee Colony (ABC) optimization techniques for the estimation of monthly SL values in the Çoruh River in Northeastern Turkey. The estimation of SL values was achieved using inputs of previous SL and streamflow values provided to the models. Various statistical metrics were used to evaluate the accuracy of the established hybrid and stand-alone models. The hybrid model is a novel approach for estimating sediment load based on various input variables. The results of the analysis determined that the ABC-ANN hybrid approach outperformed others in SL estimation. In this study, two combinations, M1 and M2, with different input variables, were used to assess the model's accuracy, and the best-performing model for monthly SL estimation was identified. Two scenarios, Q(t) and Q(t − 1), were coupled with the ABC-ANN algorithm, resulting in a highly effective hybrid approach with the best accuracy results (R2 = 0.90, RMSE = 1406.730, MAE = 769.545, MAPE = 5.861, MBE = − 251.090, Bias Factor = − 4.457, and KGE = 0.737) compared to other models. Furthermore, the utilization of FA and ABC optimization techniques facilitated the optimization of the ANN model parameters. The significant results demonstrated that the optimization and hybrid techniques provided the most effective outcomes in forecasting SL for both combination scenarios. As a result, the prediction outputs achieved higher accuracy than those of a stand-alone ANN model. The findings of this study can provide essential resources to various managers and policymakers for the management of water resources.

A novel multi-model ensemble framework for fluvial flood inundation mapping

Floods pose a significant threat to communities and infrastructure, necessitating timely predictions for effective disaster management. Conventional hydrodynamic models often encounter limitations in data requirements and computational efficiency. To overcome these constraints, we propose a novel multi-model ensemble framework integrating the flood extent and depth models for fluvial flood mapping. Various flood conditioning factors, such as terrain elevation and slope, flow direction, distance from the river, and latitude-longitude, were selected as model inputs, considering their relevance. The proposed framework was evaluated for predictive, extrapolative, and generalization capabilities. Results indicate that the proposed model successfully captures flood dynamics across a wide range of streamflow values, including unforeseen events, making it a valuable tool for predicting flood extent and depth. Overall, our approach offers a promising alternative to conventional hydrodynamic models, providing robustness, computational efficiency, scalability, automation, and integration with existing tools for flood inundation mapping tasks.

Streamflow Prediction Using Complex Networks.

The reliable prediction of streamflow is crucial for various water resources, environmental, and ecosystem applications. The current study employs a complex networks-based approach for the prediction of streamflow. The approach consists of three major steps: (1) the formation of a network using streamflow time series; (2) the calculation of the clustering coefficient (CC) as a network measure; and (3) the use of a clustering coefficient-based nearest neighbor search procedure for streamflow prediction. For network construction, each timestep is considered as a node and the existence of link between any node pair is identified based on the difference (distance) between the streamflow values of the nodes. Different distance threshold values are used to identify the critical distance threshold to form the network. The complex networks-based approach is implemented for the prediction of daily streamflow at 142 stations in the contiguous United States. The prediction accuracy is quantified using three statistical measures: correlation coefficient (R), normalized root mean square error (NRMSE), and Nash-Sutcliffe efficiency (NSE). The influence of the number of neighbors on the prediction accuracy is also investigated. The results, obtained with the critical distance threshold, reveal that the clustering coefficients for the 142 stations range from 0.799 to 0.999. Overall, the prediction approach yields reasonably good results for all 142 stations, with R values ranging from 0.05 to 0.99, NRMSE values ranging from 0.1 to 12.3, and the NSE values ranging from -0.89 to 0.99. An attempt is also made to examine the relationship between prediction accuracy and the catchment characteristics/streamflow statistical properties (drainage area, mean flow, coefficient of variation of flow). The results suggest that the prediction accuracy does not have much of a relationship with the drainage area and the mean streamflow values, but with the coefficient of variation of flow. The outcomes from this study are certainly promising regarding the application of complex networks-based concepts for the prediction of streamflow (and other hydrologic) time series.

Influence of SMAP soil moisture retrieval assimilation on runoff estimation across South Asia

This study was designed to characterize and quantify the influence of surface soil moisture assimilation on estimated runoff (surface flow and baseflow) and hydraulically-routed streamflow across three large river basins in South Asia that are at risk of impending water stress. Soil Moisture Active Passive (SMAP) surface soil moisture retrievals were assimilated into the Noah-MP land surface model. The gridded runoff was hydraulically routed to obtain volumetric streamflow values for a river network using a runoff routing module (Hydrological Modeling and Analysis Platform, HyMAP). The open loop (OL, model-only) and data assimilated (DA, includes soil moisture retrieval assimilation) Noah-MP runoff estimates highlighted the improvements in estimated total runoff across irrigated areas. Soil moisture assimilation impacted baseflow more relative to surface runoff. The HyMAP-based OL and DA streamflow generally underestimated the streamflow at upstream stations and overestimated the streamflow at downstream stations within the Indus basin due to missing physics related to reservoir operations. For stations located in the Ganges–Brahmaputra basins, the OL and DA estimation performance varied. The OL and DA results showed that the assimilation of soil moisture retrievals improves gridded runoff and volumetric streamflow across irrigated areas. Considerable relative change in streamflow (>70% increase in magnitude relative to OL) is noted after assimilation across the highly irrigated lower Indus basin and high precipitation regions in Bangladesh. Improving the modeling system’s representativeness of ground conditions via inclusion of water management information could improve large-scale streamflow modeling across South Asia.

Exploring cumulative probability functions for streamflow drought magnitude: Global scale analysis and parametric vs. non-parametric comparisons

Streamflow droughts pose significant challenges to water resource management and environmental sustainability. The accurate characterization of streamflow drought magnitude is essential for effective mitigation, assessment and adaptation strategies. In this study, we explore the implications of choosing different cumulative probability distribution functions (cdfs) on the determination of streamflow drought magnitude, as assessed by the z-score values of the standardized streamflow index (SSI) and return periods. Our investigation encompasses a comprehensive global-scale analysis, with the aim of identifying the most suitable cdfs for characterizing streamflow droughts. To accomplish this objective, we incorporate global simulated streamflow values from the model WaterGAP 2.2d and observed streamflow values from 280 Global Runoff Data Centre (GRDC) stations for the period 1981 – 2010. We evaluate the performance of 118 parametric cdfs for each of the 12 calendar monthly streamflow values at each model grid cell and GRDC station to determine the best fitting parametric cdf. Moreover, we go beyond the conventional scope of such studies by further assessing the best-fitting cdfs alongside non-parametric cdfs such as the Empirical cumulative distribution function (ecdf) and the cumulative distribution function derived from the Kernel density estimation function (kcdf). Among our findings, we establish a subset of 23 parametric cdfs that exhibit the best fit for the global streamflow values concerning all 12 calendar monthly streamflow values at individual GRDC stations or grid cells within the specified period. Furthermore, we observe that the accuracy of streamflow simulation and the length of the reference period directly influence the selection of the most suitable parametric cdf. Our comparative analysis reaffirms the existing understanding that opting for the best-fitting parametric cdf, rather than a non-parametric alternative, mitigates the risk of overestimating drought magnitudes. However, we emphasize the crucial importance of considering the intended purpose, approach-specific sensitivities, and uncertainties when choosing the most suitable parametric cdf for any calendar monthly streamflow values.

Assessing groundwater drought vulnerability through baseflow separation and index-based analysis under climate change projections

Groundwater is an essential resource, providing drinking water to millions of people globally and supporting agriculture, industry, and ecosystems. It acts as a drought buffer and provides a stable source of water during dry periods. However, groundwater is often underestimated and overexploited, leading to aquifer depletion and other negative impacts. Groundwater is invisible and difficult to measure, which makes it challenging for sustainable management. This study employed the Soil & Water Assessment Tool (SWAT) hydrological model to simulate streamflow in an eastern Afghan watershed using satellite-based and reanalysis data. Variables from five Coupled model intercomparison project phase 6 (CMIP6) general circulation models (GCMs), including minimum and maximum temperature and precipitation, were bias-corrected (downscaled) using Quantile Mapping (QM). The results from these five GCMs were then compared based on statistical criteria to determine the most reliable model, and the selected model was used to derive future data (i.e., 2021 to 2050) under different climate scenarios after applying QM. Using the future climate data and the developed SWAT model, streamflow values were projected for future periods. Considering that there are various methods to estimate groundwater recharge and the most commonly used technique involves baseflow separation procedures, this study used seven baseflow separation methods to estimate groundwater recharge values based on streamflow time series from the baseline period and different climate scenarios. To evaluate the groundwater drought characteristics, a recently proposed index called the Groundwater Recharge Drought Index (GRDI) and the run theory method were used. Results indicated that the SWAT model performed well in simulating and predicting streamflow. The sixth version of the Model for Interdisciplinary Research on Climate (MIROC6) provided the highest accuracy compared to other climate models. Moreover, it was observed that the baseflow separation methods significantly affected the estimation of groundwater recharge, thus the obtained results from all developed methods were considered. The log-normal function was found to provide the best correlation for estimating GRDI. The findings revealed a gradual decrease in drought duration in the coming years. However, the magnitude and severity of droughts are projected to increase, especially under pessimistic climate scenarios.

Investigation of climate change impacts on daily streamflow extremes in Eastern Black Sea Basin, Turkey

This study was undertaken to evaluate how future uncertain climate-related hydrological responses and low and highflow frequencies affect local hydrology up to the 2100, depending on different Global Circulation Models (GCM) and different Concentration Scenarios (CS). To this end, daily total precipitation and daily mean temperature data were used for RCP4.5 and RCP8.5 CSs by employing GCMs (GFDL-ESM2M, HadGEM2-ES and MPI-ESM-MR). In the study, the years 1971–2000 were chosen as the baseline period. Future streamflow predictions were modeled by machine learning. The models were calibrated with baseline period data and future streamflow values were predicted by using the best Streamflow Prediction Model during the 2055s (2041–2070) and 2085s (2071–2100) periods. Flow Duration Curves from the streamflow predictions were obtained and by using them, discharges corresponding to 95% probability of exceedance for lowflow and 5% probability of exceedance for highflow were calculated. According to the findings, it is predicted that there will be decrease of approximately 21% in lowflow discharges and approximately 30% in highflow discharges. This prediction has suggested water resources managers to implement mitigation measures to climate change in Eastern Black Sea Basin and most probably in Türkiye, especially in the context of continuity of aquatic life and sustainable water supply. It is expected that the paper will take a very important place in estimating and evaluating the expected changes in basin hydrology in the coming years, especially in the highflows and lowflows, which are the key concepts in hydroelectric power generation and in flood management.

Assessing the reliability of a physical-based model and a convolutional neural network in an ungauged watershed for daily streamflow calculation: a case study in southern Portugal

The main goal of this study was to estimate inflows to the Maranhão reservoir, southern Portugal, using two distinct modeling approaches: a one-dimensional convolutional neural network (1D-CNN) model and a physically based model. The 1D-CNN was previously trained, validated, and tested in a sub-basin of the study area where observed streamflow values were available. The trained model was here subject to an improvement and applied to the entire watershed by replacing the forcing variables (accumulated and delayed precipitation) to make them correspond to the values of the entire watershed. The same way, the physically based MOHID-Land model was calibrated and validated for the same sub-basin, and the calibrated parameters were then applied to the entire watershed. Inflow values estimated by both models were validated considering a mass balance at the reservoir. The 1D-CNN model demonstrated a better performance in simulating daily values, peak flows, and the wet period. The MOHID-Land model showed a better performance in estimating streamflow values during dry periods and for a monthly analysis. Hence, results show the adequateness of both modeling solutions for integrating a decision support system aimed at supporting decision-makers in the management of water availability in an area subjected to increasing scarcity.

Deep learning modeling framework for multi-resolution streamflow generation

Abstract Generating continuous streamflow information through integrated climate-hydrology modeling at fine spatial scales of the order of a few kilometers is often challenged by computational costs associated with running high-resolution (HR) climate models. To address this challenge, the present study explores deep learning approaches to generate HR streamflow information from that at low resolution (LR), based on runoff generated by climate models. Two sets of daily streamflow simulations spanning 10 years (2011–2020), at LR (50 km) and HR (5 km), for the Ottawa River basin, Canada, are employed. The proposed deep learning model is trained using upscaled features derived from LR streamflow simulation for the 2011–2018 period as input and the corresponding HR streamflow simulation as the target; data for 2019 are used for validation. The model estimates for the year 2020, when compared with unseen HR data for the same year, suggest good performance, with differences in monthly mean values for different accumulation area categories in the −0.7–5% range and correlation coefficients for streamflow values for the same accumulation area categories in the 0.92–0.96 range. The developed framework can be ported to other watersheds for generating similar information, which is required in climate change adaptation studies.

Past and Projected Future Droughts in the Upper Colorado River Basin

AbstractDrought has affected much of the western United States since about the year 2000, including the Upper Colorado River Basin (UCRB). Using a time series of UCRB streamflow derived from a tree‐ring based reconstruction of UCRB streamflow for the years 1 CE through 1905 CE, together with naturalized UCRB streamflow values for 1906 CE through 2021 CE, we identify 51 drought events, including the 2000–2021 drought. Although the recent 2000–2021 drought has been relatively severe, it is not the most severe of the UCRB drought events we identified. Results also indicate that natural variability combined with projected climate warming can result in UCRB drought events that are more severe than any drought since 1 CE.

Ensemble projections of climate and streamflow in a typical basin of semi-arid steppes in Mongolian Plateau of 2021–2100

The Kherlen River is the main water source for Hulun Lake, the largest lake in northern China. Due to reduced inflow from the Kherlen River, Hulun Lake experienced rapid shrinkage at the beginning of the 21st century, posing a serious threat to the ecological security of northern China. However, there is still a significant lack of projections regarding future climate change and its hydrological response in the Kherlen River basin. This study analyzed the projected climate and streamflow changes in the Kherlen River basin, a vital yet vulnerable international semi-arid steppes type basin. A combination of multi-model ensemble projection techniques, and the soil and water assessment tool (SWAT) model was employed to examine the spatio‒temporal changes in precipitation, temperature, streamflow, and the associated uncertainties in the basin. The temperature (an increase of 1.84–6.42 °C) and the precipitation (an increase of 15.0–46.0 mm) of Kherlen River basin are projected to increase by 2100, leading to a rise in streamflow (1.08–4.78 m3 s−1). The upstream of the Kherlen River exhibits remarkable increasing trends in precipitation, which has a dominant influence on streamflow of Kherlen River. Noteworthy increases in streamflow are observed in April, August, September, and October compared to the reference period (1971–2000). These findings suggest a partial alleviation of water scarcity in the Kherlen River, but also an increased likelihood of hydrological extreme events. The projected temperature increase in the Kherlen River basin exhibits the smallest uncertainty, while more pronounced uncertainties are found in precipitation and streamflow. The spread among the results of CMIP6 models is greater than that of CMIP5 models, with lower signal-to-noise ratio (SNR) values for temperature, precipitation, and streamflow.

Leveraging historic streamflow and weather data with deep learning for enhanced streamflow predictions

ABSTRACT Streamflow information is crucial for effectively managing water resources. The declining number of active gauging stations in many rivers is a global concern, necessitating the need for reliable streamflow estimates. Deep learning techniques offer potential solutions, but their application in southern Africa remains largely underexplored. To fill this gap, this study evaluated the predictive performance of gated recurrent unit (GRU) and long short-term memory (LSTM) networks using two headwater catchments of the Steelpoort River, South Africa, as case studies. The model inputs included rainfall, maximum, and minimum temperature, as well as past streamflow, which was utilized in an autoregressive sense. The inclusion of streamflow in this way allowed for the incorporation of simulated streamflow values into the look-back window for predicting the streamflow of the testing set. Two modifications were required to the GRU and LSTM architectures to ensure physically consistent predictions, including a change in the activation function of the GRU/LSTM cells in the final hidden layer, and a non-negative constraint that was used in the dense layer. Models trained using commercial weather station data produced reliable streamflow estimates, while moderately accurate predictions were obtained using freely available gridded weather data.

Changes in the hydrological regime and Climatic variability assessment within the Belaya River catchment, Russia

The trends of changes in the indicators of the hydrological regime and climatic variability in the catchment area of the Belaya River were assessed. Mann-Kendall test and Sen's slope estimator at 8 out of 10 (80%) in the considered posts showed the presence of a positive trend in changes in the average monthly values of streamflow. At the same time, meteorological data sets at 19 of 34 (56%) observation stations have a positive trend of changes in the average monthly precipitation and temperature.

Which range of streamflow data is most informative in the calibration of an hourly hydrological model?

ABSTRACT From which part of the streamflow time series does a hydrological model learn most about the behaviour of a catchment? How does this part change with the objective function used for model calibration? We calibrated the hourly model GR4H (Génie Rural à 4 paramètres au pas de temps Horaire) on data subsets selected based on the flow duration curve, which narrowed the range of streamflow that influenced the identification of model parameters the most. Using 658 catchments in the United States and France, we found that calibrating GR4H on the highest 50% of streamflow values of a given period provided similar parameters to calibration on the whole period, with the top 10% dominating the identification of parameters. The logarithmic transformation of streamflow balanced the contribution from high and low flows, but only the reciprocal transformation emphasized low flows. The proposed methodology identifies the informative ranges of streamflow data and allows anticipating potential model failures in nonstationary hydroclimatic conditions.

Comparison of Monte Carlo Schemes in the Modeling of Extreme Flood in Tropical Rain Forest Basins

Insufficient length of streamflow record poses challenges in capturing extreme events, frequencies, and sequences crucial for effective water resources systems design. Consequently, simulation and application of statistically similar streamflow values becomes imperative. In this study, two Markov-based models, namely the Thomas-Fiering (T-F) model and the Autoregressive (AR) model, were developed based on a log-normal probability distribution to synthesize monthly and annual flows using a decade of historical data (1980–1989) collected from the Umulokpa gauging station in the Adada River catchment of Enugu State, Nigeria. Except for the month of December (p = 0.03), no significant difference (p > 0.05) was observed between the monthly and annual model-predicted streamflows. However, both models demonstrated limitations in fully capturing the characteristics of the observed flows, particularly regarding Kurtosis. Further accuracy assessment from computed correlation coefficients (0.018 for T-F and 0.122 for AR), along with root mean square error (RMSE) values (12.84 for T-F and 11.95 for AR), indicate that while both models deviated from the observed flows, the AR model demonstrated superior performance compared to the T-F model. However, to apply these models reliably, for future flow prediction in the Adada catchment, streamflow data from rivers with comparable catchment characteristics is essential.