Ungauged Catchments Research Articles

Hydrological Modeling: A Better Alternative to Empirical Methods for Monthly Flow Estimation in Ungauged Basins

Water resource is required for agricultural, industrial, and domestic activities and for environmental preservation. However, with the increase in population and growth of urbanization, industrialization, and commercial activities, planning and management of water resources have become a challenging task to meet various water demands globally. Information and data on streamflow hydrology are, thus, crucial for this purpose. However, availability of measured flow data in many cases is either inadequate or not available at all. When there is no gauging station available at the site of interest, various empirical methods are generally used to estimate the flow there and the best estimation is chosen. This study is focused on the estimation of monthly average flows by such methods popular in Nepal and assessment of how they compare with the results of hydrological simulation. Performance evaluation of those methods was made with a newly introduced index, Global Performance Index (GPI) utilizing six commonly used goodness-of-fit parameters viz. coefficient of determination, mean absolute error, root mean square error, percentage of volume bias, Nash Sutcliff Efficiency and Kling-Gupta Efficiency. This study showed that hydrological modeling is the best among the considered methods of flow estimation for ungauged catchments.

Comparison of RUSLE and MMF Soil Loss Models and Evaluation of Catchment Scale Best Management Practices for a Mountainous Watershed in India

Soil erosion from arable lands removes the top fertile soil layer (comprised of humus/organic matter) and therefore requires fertilizer application which affects the overall sustainability. Hence, determination of soil erosion from arable lands is crucial to planning conservation measures. A modeling approach is a suitable alternative to estimate soil loss in ungauged catchments. Soil erosion primarily depends on soil texture, structure, infiltration, topography, land uses, and other erosive forces like water and wind. By analyzing these parameters, coupled with geospatial tools, models can estimate storm wise and annual average soil losses. In this study, a hilly watershed called Nongpoh was considered with the objective of prioritizing critical erosion hazard areas within the micro-catchment based on average annual soil loss and land use and land cover and making appropriate management plans for the prioritized areas. Two soil erosion models namely Revised Universal Soil Loss Equation (RUSLE) and Modified Morgan–Morgan–Finney (MMF) models were used to estimate soil loss with the input parameters extracted from satellite information and automatic weather stations. The RUSLE and MMF models showed similar results in estimating soil loss, except the MMF model estimated 7.74% less soil loss than the RUSLE model from the watershed. The results also indicated that the study area is under severe erosion class, whereas agricultural land, open forest area, and scrubland were prioritized most erosion prone areas within the watershed. Based on prioritization, best management plans were developed at catchment scale for reducing soil loss. These findings and the methodology employed can be widely used in mountainous to hilly watersheds around the world for identifying best management practices (BMP).

Review: Sources of Hydrological Model Uncertainties and Advances in Their Analysis

Despite progresses in representing different processes, hydrological models remain uncertain. Their uncertainty stems from input and calibration data, model structure, and parameters. In characterizing these sources, their causes, interactions and different uncertainty analysis (UA) methods are reviewed. The commonly used UA methods are categorized into six broad classes: (i) Monte Carlo analysis, (ii) Bayesian statistics, (iii) multi-objective analysis, (iv) least-squares-based inverse modeling, (v) response-surface-based techniques, and (vi) multi-modeling analysis. For each source of uncertainty, the status-quo and applications of these methods are critiqued in gauged catchments where UA is common and in ungauged catchments where both UA and its review are lacking. Compared to parameter uncertainty, UA application for structural uncertainty is limited while input and calibration data uncertainties are mostly unaccounted. Further research is needed to improve the computational efficiency of UA, disentangle and propagate the different sources of uncertainty, improve UA applications to environmental changes and coupled human–natural-hydrologic systems, and ease UA’s applications for practitioners.

A Review of Tank Model and Its Applicability to Various Korean Catchment Conditions

This paper reviews a conceptual rainfall-runoff model called Tank which has been widely used over the last 20 years in Korea as a part of a water resource modelling framework for assessing and developing long-term water resource polices. In order to examine the uncertainty of model predictions and the sensitivity of model’s parameters, Monte Carlos and Markov chain-based approaches are applied to five catchments of various Korean geographical and climatic conditions where the catchment sizes are ranged from 83 to 4786 km2. In addition, three optimization algorithms—dynamically dimensioned search (DDS), robust parameter estimation (ROPE), and shuffled complex evolution (SCE)—are selected to test whether the model parameters can be optimized consistently within a narrower range than the uncertainty bounds. From the uncertainty analysis, it is found that there is limited success in refining the priori distributions of the model parameters, indicating there is a high degree of equifinality for some parameters or at least there are large numbers of parameter combinations leading to good solutions within model’s uncertainty bounds. Out of the three optimization algorithms, SCE meets the criteria of the consistency best. It is also found that there are still some parameters that even the SCE method struggles to refine the priori distributions. It means that their contribution to model results is minimal and can take a value within a reasonable range. It suggests that the model may be reconceptualized to be parsimonious and to rationalize some parameters without affecting model’s capacity to replicate historical flow characteristics. Cross-validation indicates that sensitive parameters to catchment characteristics can be transferred when geophysical similarity exists between two catchments. Regionalization can be further improved by using a regression or geophysical similarity-based approach to transfer model parameters to ungauged catchments. It may be beneficial to categorize the model parameters depending on the level of their sensitivities, and a different approach to each category may be applied to regionalize the calibrated parameters.

Regional Modeling of Long-Term and Annual Flow Duration Curves: Reliability for Information Transfer with Evolutionary Polynomial Regression

AbstractThe estimation of long-term flow duration curves (FDC) and annual flow duration curves (AFDC) are frequently required for water resources planning and management at ungauged catchments. The...

Regional Flood Frequency Analysis using Dimensionless Index Flood Method

Hydrologic designs require accurate estimation of quartiles of extreme floods. But in many developing regions, records of flood data are seldom available. A model framework using the dimensionless index flood for the transfer of Flood Frequency Curve (FFC) among stream gauging sites in a hydrologically homogeneous region is proposed. Key elements of the model framework include: (1) confirmation of the homogeneity of the region; (2) estimation of index flood-basin area relation; (3) derivation of the regional flood frequency curve (RFFC) and deduction of FFC of an ungauged catchment as a product of index flood and dimensionless RFFC. As an application, 1983 to 2004 annual extreme flood from six selected gauging sites located in Anambra-Imo River basin of southeast Nigeria, were used to demonstrate that the developed index flood model: , overestimated flood quartiles in an ungauged site of the basin. It is recommended that, for wider application, the model results can be improved by the availability and use of over 100 years length of flood data spatially distributed at critical locations of the watershed. Doi: 10.28991/cej-2020-03091627 Full Text: PDF

Climate Change Impact on Hydropower Resources in Gauged and Ungauged Lithuanian River Catchments

Hydropower (potential and kinetic energy) is one of the most important renewable energy sources in the world. This energy is directly dependent on water resources and the hydrological cycle. Ongoing climate changes are likely to influence the availability/amount of this energy resource. The present study explores the relationship between climate changes and river runoff, projects future runoff in both gauged and ungauged river catchments, and then assesses how these alterations may affect the future hydropower resources in Lithuania. Runoff projections of the gauged rivers were evaluated applying Swedish Department of Climate hydrological model, and runoff of ungauged river catchments were estimated by created isoline maps of specific runoff. According to an ensemble of three climate models and two Representative Concentration Pathway scenarios, runoff and hydroelectric energy projections were evaluated for two future periods (2021–2040, 2081–2100). The results demonstrated a decrease in future river runoff. Especially significant changes are expected according to the most pessimistic RCP8.5 scenario at the end of the century. The projected changes are likely to bring a negative effect on hydropower production in the country. These findings could help understand what kind of benefits and challenges water resource managers may face in the future.

A Novel Fuzzy Random Forest Model for Meteorological Drought Classification and Prediction in Ungauged Catchments

This paper presents a new tree-based model, namely Fuzzy Random Forest (FRF), for one month ahead Standardized Precipitation Evapotranspiration Index (SPEI) classification and prediction with a noteworthy application in ungauged catchments. The proposed FRF model uses global SPEI dataset as the meteorological drought quantifier and applies a fuzzy inference system to extract fuzzified and crisp SPEI values for an ungauged catchment. The evolved crisp series is then transformed into the polynomial label vector of extremely wet, wet, near normal, dry, and extremely dry categories. In the end, the state-of-the-art random forest algorithm was used to classify and predict the label vector using the lagged SPEI series of the selected global grid points. To demonstrate the development and verification process of the FRF model, the global SPEI-6 values for the period of 1961–2015 were retrieved from four global grid points around the Central Antalya Basin, Turkey. The new model was trained and validated using 70% and 30% of the data sets, respectively. The performance of the new model was examined in terms of total accuracy, misclassification, and Kappa statistics and cross-validated with the fuzzy decision tree model developed as the benchmark in this study. The results showed the promising performance of the FRF for drought classification and prediction with outstanding efficiency for extremely wet and dry events classification. According to the Kappa statistic, the proposed FRF model is 25% more accurate than the benchmark FDT model.

Modeling Hydrological Processes in Ungauged Snow-Fed Catchment of Western Himalaya

Modeling Hydrological Processes in Ungauged Snow-Fed Catchment of Western Himalaya

Regionalization of mean annual flow for ungauged catchments in case of Abbay River Basin, Ethiopia

Streamflow measurement is one of the features of Ethiopia and other developing countries suffer to estimate. Climate variable (rainfall) and physiographic variables (catchment area, land slope, and elevation) were selected to develop a regression model equation that can be used to estimate the mean annual flow for ungauged catchments in the Abbay river basin. 70% of 27 hydrometric stations were used to train the regression model, and the remaining 30% were used for validation. Furthermore, statistical tests were used for selecting the best trustworthy model. The result shows that both catchment area (A) as the only predictor with both coefficients of determination (R2) and Nash–Sutcliffe efficiency (NSE) value of 0.93 and catchment area (A), mean annual rainfall (MAR), and the average elevation (AE) as predictors with both coefficients of determination (R2) and Nash–Sutcliffe efficiency (NSE) value of 0.96 regression equations were the trustworthy models. Moreover, the validation analysis was performed for nine stations distributed in the study area and resulting from a statistical test and graphical visualization the second regression model equation was a trustworthy model. The model parameters (catchment characteristics) are easily accessible for practitioners that want to estimate the mean annual flow in the study area.

Palaeoflood discharge estimation using dendrogeomorphic methods, rainfall-runoff and hydraulic modelling—a case study from southern Crete

This study represents the first palaeoflood discharge estimation using dendrogeomorphic evidence from two ungauged catchments in southern Crete. Dendrogeomorphological methods, rainfall-runoff and hydraulic modelling were applied to estimate the palaeoflood discharge of the 2000 flood event. We developed two scenarios (QHydraulic, QRR) of peak discharges in the short reaches of the Ilingas and Sfakia catchments. Scenario QRR was based on the results of the uncalibrated rainfall-runoff model HEC-HMS. The calculated discharges were equal to 35.9 m3 s−1 in the Ilingas reach and 7.6 m3 s−1 in the Sfakia reach. The second scenario, QHydraulic was based on the output of the two-dimensional hydraulic model IBER, which was calibrated using six palaeostage indicators obtained during a field survey. The reconstructed discharge for the QHydraulic scenario was equal to 26.3 m3 s−1 in Ilingas and 11.2 m3 s−1 in Sfakia. Deviation (26.7% in Ilingas and 32.1% in Sfakia) between the scenarios were attributed mainly to the uncertainties of in flood reconstruction using the palaeostage indicators and uncalibrated rainfall-runoff model. We further investigated uncertainty in Manning’s roughness coefficient, which caused deviations in peak discharges with the range of these deviations defined within ± 40% in the Ilingas reach and ± 36% in the Sfakia reach.

Regionalization of hydrological modeling for predicting streamflow in ungauged catchments: A comprehensive review

AbstractRunoff prediction in ungauged and scarcely gauged catchments is a key research field in surface water hydrology. There have been numerous studies before and since the launch of the predictions in ungauged basins (PUB) initiative by the International Association of Hydrological Sciences in 2003. This study critically reviews and assesses the decadal progress in the regionalization of hydrological modeling, which is the major tool for PUB, from 2000 to 2019. This paper found that the journal publications have noticeably increased in terms of PUB in the past 7 years, and research countries have been expanded dramatically since 2013. The regionalization methods are grouped into three categories including similarity‐based, regression‐based, and hydrological signature‐based. There are more detailed researches focusing on the interdisciplinary and profound improvement of each regionalization method. Namely, tremendous efforts have been made and lots of improvements have been carried out in the parameterization domain for the post‐PUB period. However, there is still plenty of room to improve the prediction capability in data‐sparse regions (e.g., further verification and proof of multi‐modeling adaptation and uncertainties description). This paper also discusses possible research directions in the future, including PUB in a changing environment and better utilization of multi‐source remote‐sensing information.This article is categorized under: Science of Water > Science of Water

Flood hazard delineation in an ungauged catchment by coupling hydrologic and hydraulic models with geospatial techniques-a case study of Koraiyar basin, Tiruchirappalli City, Tamil Nadu, India.

Flooding in urban basins is a major natural catastrophe that leads to many causalities of life and property. The semi-urbanized Koraiyar River basin in Tamil Nadu has important cities like Tiruchirappalli and many towns located in it. The basin unfailingly experiences a flood event in almost every decade. It is anticipated that the basin will undergo rapid unplanned urbanization in the years to come. Such fast and erratic urban developments will only increase the risk of urban floods ultimately resulting in loss of human lives and extensive damages to property and infrastructure. The effects of urbanization can be quantified in the form of land use land cover (LULC) changes. The LULC change and its impacts on urban runoff are studied for the continuous 30-year present time period of (1986-2016) to reliably predict the anticipated impact in the future time period of (2026-2036). The analysis of land cover patterns over the years shows that urbanization is more prevalent in the northern part of the basin of the chosen study area when compared with the other regions. The extreme rainfall events that occurred in the past, and the probable future LULC changes, as well as their influence on urban runoff, are studied together in the current study. In order to minimize flood damages due to these changing land use conditions, certain preventive and protective measures have to be adopted at the earliest. There are some inevitable limitations while applying traditional measures in flood modeling studies. This investigative work considers a case study on the ungauged Koraiyar floodplains. The spatial scale risk assessment is assessed by coupling geographic information systems, remote sensing, hydrologic, and hydraulic modeling, to estimate the flood hazard probabilities in the Koraiyar basin. The maximum flood flow is generated from the Hydrologic Engineering Centre-Hydrologic Modeling System (HEC-HMS), the hydrologic model adopted in the present study. The maximum flood flow is given as input to the Hydrologic Engineering Centre-River Analysis System (HEC-RAS), an effective hydraulic model that generates water depth and flood spread area in the basin. The flood depth and hazard maps are derived for 2, 5, 10, 50, and 100-year return periods. From the analysis, it is observed that the minimum flood depth is less than 1.2m to a maximum of 4.7m for the 100-year return period of past to predicted future years. The simulated results show that the maximum flood depth of 4.7m with flood hazard area of 4.32% is identified as high hazard zones from the years 1986-2036, located in the center of the basin in Tiruchirappalli city. The very high hazard flood-affected zone in the Koraiyar basin during this period is about 198.85km2. It is noticed that the very low hazard zone occupies more area in the basin for the present and future simulations of flood hazard maps. The results show that the increase in peak runoff and runoff volume is marginally varied.

Impact of physico‐geographical factors and climate variability on flow intermittency in the rivers of water surplus zone

AbstractClimate change is inevitably altering the hydrological regime of water bodies. The interest in changing behaviour of intermittent rivers is increasing in many countries. This research was focused on intermittent rivers (rivers which naturally, periodically cease to flow) in Lithuania. The purpose of this research was to provide an overview of flow intermittency phenomena according to available data in a historical period and to evaluate the impact of catchment geographical features and climate variability on zero‐flow events. The calculated indices of flow intermittency showed that the selected rivers had very different flow regimes. The threshold for the separation of typically intermittent rivers from only occasionally intermittent ones was suggested. Multiple linear regression analysis defined the crucial role of catchment size and watercourse slope on the river cessation process in Lithuania. The applied non‐parametric Wilcoxon–Mann–Whitney test revealed the significance of the relationship between precipitation (in June–September) and zero‐flow duration. Flow intermittency phenomena in Lithuanian rivers were linked to a low‐frequency teleconnection pattern (SCAND index). A methodology of estimating the relation between river intermittency and large‐scale atmospheric circulation pattern (based on SCAND index) was created. The generated regression equations between flow intermittency indices and catchment characteristics might be useful for the estimation of zero‐flows in ungauged river catchments. The main aspect of future investigations might be related to forecasting flow intermittency using modern hydrological models and climate scenarios as well as the defined relationships between zero‐flow indices and physico‐geographical features of river catchments.

Hydrological impacts of climate change on small ungauged catchments – results from a global climate model–regional climate model–hydrologic model chain

Abstract. Climate change is one of the greatest threats currently facing the world's environment. In Norway, a change in climate will strongly affect the pattern, frequency, and magnitudes of stream flows. However, it is challenging to quantify to what extent the change will affect the flow patterns and floods from small rural catchments due to the unavailability or inadequacy of hydro-meteorological data for the calibration of hydrological models and due to the tailoring of methods to a small-scale level. To provide meaningful climate impact studies at the level of small catchments, it is therefore beneficial to use high-spatial- and high-temporal-resolution climate projections as input to a high-resolution hydrological model. In this study, we used such a model chain to assess the impacts of climate change on the flow patterns and frequency of floods in small ungauged rural catchments in western Norway. We used a new high-resolution regional climate projection, with improved performance regarding the precipitation distribution, and a regionalized hydrological model (distance distribution dynamics) between a reference period (1981–2011) and a future period (2070–2100). The flow-duration curves for all study catchments show more wet periods in the future than during the reference period. The results also show that in the future period, the mean annual flow increases by 16 % to 33 %. The mean annual maximum floods increase by 29 % to 38 %, and floods of 2- to 200-year return periods increase by 16 % to 43 %. The results are based on the RCP8.5 scenario from a single climate model simulation tailored to the Bergen region in western Norway, and the results should be interpreted in this context. The results should therefore be seen in consideration of other scenarios for the region to address the uncertainty. Nevertheless, the study increases our knowledge and understanding of the hydrological impacts of climate change on small catchments in the Bergen area in the western part of Norway.

Using Remote Sensing Data‐Based Hydrological Model Calibrations for Predicting Runoff in Ungauged or Poorly Gauged Catchments

AbstractBecause remote sensing (RS) data are spatially and temporally explicit and available across the globe, they have the potential to be used for predicting runoff in ungauged catchments and poorly gauged regions, a challenging area of research in hydrology. There is potential to use remotely sensed data for calibrating hydrological models in regions with limited streamflow gauges. This study conducts a comprehensive investigation on how to incorporate gridded remotely sensed evapotranspiration (AET) and water storage data for constraining hydrological model calibration in order to predict daily and monthly runoff in 30 catchments in the Yalong River basin in China. To this end, seven RS data calibration schemes are explored and compared to direct calibration against observed runoff and traditional regionalization using spatial proximity to predict runoff in ungauged catchments. The results show that using bias‐corrected remotely sensed AET (bias‐corrected PML‐AET data) for constraining model calibration performs much better than using the raw remotely sensed AET data (nonbias‐corrected AET obtained from PML model estimate). Using the bias‐corrected PML‐AET data in a gridded way is much better than using lumped data and outperforms the traditional regionalization approach especially in headwater and large catchments. Combining the bias‐corrected PML‐AET and GRACE water storage data performs similarly to using the bias‐corrected PML‐AET data only. This study demonstrates that there is great potential in using bias‐corrected RS‐AET data to calibrating hydrological models (without the need for gauged streamflow data) to estimate daily and monthly runoff time series in ungauged catchments and sparsely gauged regions.

A novel method of satellite based river discharge estimation using river hydraulic geometry through genetic algorithm technique

Many countries experiences floods due to heavy flows carried by trans-boundary Rivers. Hydrological modelling in un-gauged catchments and trans-boundary Rivers has become a challenging task for hydrological modellers. This research aims at estimating river discharges computed solely using satellite images, with no ground-based or a prior information at all. It is also aimed at testing the applicability of approach in very well defined channel to braided channels and in very wide range of discharge conditions. The approach works owing to principle of hydraulic geometry of natural rivers. Monitoring these discharges in upstream areas will help in forecasting flows in the downstream areas. In this paper, an attempt is made to estimate the river discharge using river flow width information derived from temporal satellite remote sensing data alone. The approach is based on Many Stations Hydraulic Geometry (AMHG) principle through Genetic Algorithm Technique. The technique is demonstrated in four major rivers in India for various discharge values using temporal remote sensing data of Indian Remote Sensing Satellites (IRS) and LANDSAT. Computed discharges are validated with observed discharges in all the identified river reaches using statistical method and found to have very good match. This novel approach is useful globally for quantifying river discharges with continuous imaging of the rivers and has strong advantage in flood early warning and water resources assessment.

Relationships between Hydropower Generation and Rainfall-Gauged and Ungauged Catchments from Sri Lanka

The relationship between the rainfall and minihydropower generation in a catchment is highly nonlinear. Therefore, the prediction of minihydropower generation is complex. However, the prediction is important in optimizing the control of electricity generation under various environmental conditions. Ongoing climate variabilities have completely changed the minihydropower generation to some parts of the world, and it is significant. Therefore, this paper presents results from two soft-computing studies in searching the relationships between rainfall and the generated hydropower. The first study was carried out for a gauged catchment; however, the second was carried for an ungauged catchment. Results revealed that there is an acceptable correlation in between the rainfall and hydropower generation for the gauged catchment and a marginal contribution to the ungauged catchment.

Testing the Robustness of a Physically-Based Hydrological Model in Two Data Limited Inland Valley Catchments in Dano, Burkina Faso

This study investigates the robustness of the physically-based hydrological model WaSiM (water balance and flow simulation model) for simulating hydrological processes in two data sparse small-scale inland valley catchments (Bankandi-Loffing and Mebar) in Burkina Faso. An intensive instrumentation with two weather stations, three rain recorders, 43 piezometers, and one soil moisture station was part of the general effort to reduce the scarcity of hydrological data in West Africa. The data allowed us to successfully parameterize, calibrate (2014–2015), and validate (2016) WaSiM for the Bankandi-Loffing catchment. Good model performance concerning discharge in the calibration period (R2 = 0.91, NSE = 0.88, and KGE = 0.82) and validation period (R2 = 0.82, NSE = 0.77, and KGE = 0.57) was obtained. The soil moisture (R2 = 0.7, NSE = 0.7, and KGE = 0.8) and the groundwater table (R2 = 0.3, NSE = 0.2, and KGE = 0.5) were well simulated, although not explicitly calibrated. The spatial transposability of the model parameters from the Bankandi-Loffing model was investigated by applying the best parameter-set to the Mebar catchment without any recalibration. This resulted in good model performance in 2014–2015 (R2 = 0.93, NSE = 0.92, and KGE = 0.84) and in 2016 (R2 = 0.65, NSE = 0.64, and KGE = 0.59). This suggests that the parameter-set achieved in this study can be useful for modeling ungauged inland valley catchments in the region. The water balance shows that evaporation is more important than transpiration (76% and 24%, respectively, of evapotranspiration losses) and the surface flow is very sensitive to the observed high interannual variability of rainfall. Interflow dominates the uplands, but base flow is the major component of stream flow in inland valleys. This study provides useful information for the better management of soil and scarce water resources for smallholder farming in the area.

Runoff and sediment yield modeling in data-sparse catchments in the Garehsoo River basin, northern Iran

To improve management and conservation in ungauged catchments in northern Iran, we developed regional models to predict annual runoff and discharge using multi-year time series from 20 gauged catchments in Ardabil, Province. We employed correlation, Ward cluster analysis, and principal component analysis to reduce the total parameter set (69 parameters represent catchment geometry, geology, soil, rainfall, and climate) to robust sets for regression analysis. The reduction method based on PCA using non-log-transformed data produced the best results, based on several error metrics (modified Nash Sutcliffe efficiency, R2, root mean squared error, and bias). Important parameters included area and mean annual precipitation, as well as those indicative of catchment shape and geology. Despite excellent ability to simulate mean annual discharge and sediment yield in the 20 catchments, the models performed poorly when applied to estimating year-to-year discharge and sediment yield in individual catchments. Poor prediction likely resulted from (a) rainfall data of insufficient spatial resolution in the large area with greatly varying elevation including mountains; and (b) inability to include data reflecting yearly changes in land-cover/land-use. Nevertheless, the regional models are likely useful for estimating mean runoff and sediment yields in other catchments in the region with similar geology, geomorphology, and climate.