Hydrological Modelling Results Research Articles

Impact Assessment of the Potential Evapotranspiration Method on Results of Hydrological Modeling

Impact Assessment of the Potential Evapotranspiration Method on Results of Hydrological Modeling

Enhancing Model Calibration and Uncertainty Estimation through Multi-Objective Optimization with SUFI-2 Algorithm

The research aimed to quantify the uncertainty in hydrological modeling results, especially in data-scarce areas like the Teesta River. The developed grid-based model from Texas Agricultural and Management University (TAMU) with the help of simulation plugin Quantum Soil and Water Assessment Tools (QSWAT) and calibration (CAL) & validation (VAL) using SWAT-Calibration Uncertainty Program (CUP). Multiple algorithms Minimize & Maximize (MIN & MAX), specifically with Sequential Uncertainty Fitting Version-2 (SUFI-2) algorithm (Best fitted simulation), were used as the popular for the many ideal simulations as possible that are within 95% of the true predictions. The research investigated how eleven different objective functions (OBJF) influenced the CAL results, parameterization, and surface water estimation for the stream flow of the Teesta River. While several OBJF performed well, eight of them, namely Chi-square (Chi2), Multiplicative (Multi), Modified Nash-Sutcliffe efficiency factor (MNSE), Nash-Sutcliffe efficiency (NSE), summation (SUM), Coefficient of determination (R2), Modified coefficient of determination (bR2), and Relative Squared Error (RSR), consistently yielded very similar results because of mostly used MIN methods. For instance, NSE resulted in a CAL & VAL score of 0.85 and 0.88, while R2, MNSE, and bR2 had scores of 0.88 and 0.89 during both CAL & VAL periods. All results from NSE, R2 and bR2 show a correlation between 0.84 to 0.90. However, other OBJF provided slightly different sensitivity values. The Percentage Bias (PBIAS) scored 0.76 for CAL and 0.77 for VAL. The Kling-Gupta efficiency (KGE) scored 0.79 during both the CAL & VAL periods. Additionally, the sum of squares (SSQR) obtained shallow values for both CAL & VAL processes. The aforementioned research shows that varied OBJF can affect CAL findings and hydrological modelling parameter values, emphasizing the need to use several OBJF in uncertainty analysis for more accurate water resource estimation the calibrated model and its outputs aid hydrologists and water resource administrators in agricultural and water assessment. They also support research on climate change's effects on water supply and quality, droughts, and food. The unique idea and flexible techniques make them suitable for most countries.

The use of GRDC gauging stations for calibrating large-scale hydrological models

Abstract. The Global Runoff Data Centre (GRDC) provides time series of observed discharges and information on hydrometric stations that are valuable for calibrating and validating the results of hydrological models. We address a common issue in large-scale hydrology that has not been satisfactorily solved, though investigated several times. To compare simulated and observed discharge, grid-based hydrological models must fit reported station locations to the resolution-dependent gridded river network. We introduce an intersection-over-union ratio approach to selected station locations on a coarser grid scale, reducing the errors in assigning stations to the correct upstream basin. We update the 10-year-old database of watershed boundaries with additional stations based on a high-resolution (3 arcsec) river network and provide source codes and high- and low-resolution watershed boundaries to easily select stations for calibration/validation of hydrological models. The dataset is stored on Zenodo with the associated DOI: https://doi.org/10.5281/zenodo.6906577 (Burek and Smilovic, 2022).

Enhanced machine learning model via twin support vector regression for streamflow time series forecasting of hydropower reservoir

The non-stationary, complex, and non-linear characteristics of streamflow time series have a significant impact on the simulation results of the conventional hydrological forecasting models. To improve the performances, this paper develops an enhanced machine learning model for streamflow time series forecasting, where the twin support vector regression (TSVR) is combined with singular spectrum analysis (SSA) and grey wolf optimizer (GWO). Specially, the SSA method is set as the data preprocessing tool for pattern identification; the TSVR model is set as the basic forecasting module for each pattern and the GWO method is used as the optimizer to select feasible parameter combination. Multi-step-ahead streamflow forecasting tasks are used to examine the feasibility and predictability of the proposed model. The results indicate that the proposed model can yield superior results compared with the traditional forecasting models. Thus, a robust and reliable tool is provided for streamflow time series forecasting under uncertainty.

Water Dynamics Analysis in Karst Flood Areas Using Sentinel-1 Time Series

Studying karst water dynamics is challenging because of the often unknown underground flows. Therefore, studies of visible karst waters receive considerable research emphasis. Researchers are turning to various data sources, including remote sensing imagery, to study them. This research paper presents an assessment of a water bodies dataset, automatically detected from Sentinel-1 imagery, for karst flood research. Statistical and visual analyses were conducted to assess the reliability and effectiveness of the dataset. Spearman’s correlation coefficients were employed for statistical analysis to determine the degree of correlation between the areas of water bodies dataset and official water level data. Visual analyses involved the creation of heat maps based on the identified water areas, which were then compared to official flood maps, and the preparation of an analysis of historical flood events or results of hydrological and hydraulic modelling. Additionally, vegetation maps were produced to identify areas that lacked detection and complemented the heat maps. Statistical assessment showed a strong correlation (≥0.6) between the dataset and official water level data in smaller flood-prone areas with less complex inflow. Visual analyses using heat maps and vegetation maps effectively identified frequently flooded areas but had limitations in areas with dense vegetation. Comparisons with flood maps showed an important value of the dataset as an additional source of information for karst flood studies. This assessment highlights the dataset’s potential in combination with other data sources and modelling approaches.

SEDIMENT TRANSPORT ESTIMATION FROM HYDROLOGICAL AND AUTOREGRESSIVE MODELS

Hydrologic modeling allows the simulation of runoff and sediment processes, which are applied in integrated watershed management, soil and water nutrients, among others. However, these models require considerable amounts of input data. Sediment data is often lacking in quantity and quality, which leads to uncertainty in hydrological models. The objective of the present study was to propose a methodological alternative based on sediment time series in the Santa Cruz de Aquismón sub-basin, San Luis Potosí, Mexico, by means of autoregressive integrated moving average models (ARIMA) and the Soil and Water Assessment Tool (SWAT) model. The SWAT model was calibrated and validated with measured flows from the National Surface Water Data Bank (BANDAS) of station 26 241 (Ballesmi). Model calibration and validation performance was assessed with Nash-Sutcliffe Coefficient (NSE), percent bias (PBIAS), and the root mean squared error (RMSE). The SWAT model fit was rated as very good. The hydrologic model results were compared to the daily sediment estimates from three months in 1985 (June, September, and November) obtained from ARIMA models. The mean absolute percent error (MAPE) was 0.571, 0.168, and 0.029, respectively. The results indicated that the use of the ARIMA model for sediment estimation is useful when there are short time series with limited information, since it allows the completion of missing data series or short-term estimates.

Soil Properties as Indicators of Soil Moisture Regime Parameters (a Review)

The water movement in soils contributes to the formation of a number of specific soil properties; their interpretation allows for to characterization of the features of both individual hydrological processes and the soil water regime, which is especially important due to the limited possibilities of soil wetness monitoring. The review considers the diagnostic indicators of mineral soils, determined in the field and laboratory conditions, characterized by different formation times and resistance to changes in environmental factors. Field methods for diagnosing soil moisture regime parameters make it possible to formulate hypotheses about the features of water behavior within the soil profile, which can later be confirmed or refuted as a result of laboratory diagnostic methods, regime observations, and physical and mathematiqcal modeling. In particular, on the basis of a standard field description of soils, it is possible to identify the zone of the greatest moisture turnover, areas of the soil profile, which are characterized by constant and periodic moisture stagnation, as well as determining the level of occurrence of the capillary fringe. Clay coats, root burrows, and soil animals paths mark the preferental flows. Laboratory diagnostic methods are aimed at assessing the quantitative ratio of hydrological processes in the soil; basically, they allow to characterize the source, duration and intensity of the period of soil overmoistering. The Schwertman criterion is the most commonly used analytical indicator for diagnosing soil overmoistering, the possibility and effectiveness of which has been shown for a wide range of soils. Verification of the results of hydrological modeling by soil properties is potentially possible for the processes of downward and lateral runoff, physical evaporation of moisture, transpiration, evapotranspiration and capillary rise.

Quality assessment of hydrometeorological observational data and their influence on hydrological model results in Alpine catchments

ABSTRACT Hydrometeorological observational data can be affected by trends, change points and inhomogeneities. It is often difficult to identify the cause of these statistical properties of the time series and therefore they represent a source of uncertainty that can propagate to the hydrological model results. We examine two case studies of high-elevation Alpine catchments for which 59 hydrometeorological time series are analysed to show how often trends, change points and inhomogeneities in the time series occur. We consider daily and monthly aggregated data, as well as a set of nine homogeneity tests, six trend tests and three change-point tests. The results show that inhomogeneities in meteorological time series have a stronger and more direct influence on the cryosphere (snow and glacier processes) than on runoff generation. The non-linear behaviour of hydrological processes greatly complicates the judgement about the consequences of including or excluding suspicious time series in hydrological model set-ups.

Unstructured-grid approach to develop high-fidelity groundwater model to understand groundwater flow and storage responses to excessive groundwater withdrawals in the Southern Hills aquifer system in southeastern Louisiana (USA)

Study regionThe Southern Hills aquifer system (SHAS) in the Louisiana Capital Area Groundwater Conservation District (CAGCD), USA. Study focusThe SHAS provides abundant groundwater for public and industrial supplies in the CAGCD. Groundwater depletion, saltwater intrusion, and land subsidence are potential concerns due to prolonged excessive groundwater withdrawals. This study develops a high-fidelity groundwater flow model utilizing a complex unstructured grid to investigate groundwater flow and storage responses to excessive groundwater withdrawals for the SHAS in the CAGCD. The groundwater model incorporates the Mississippi River alluvial aquifer down to the Miocene sands extending to depths around 1 km. New hydrological insightsGroundwater modeling results indicate large cones of depression in the Evangeline and Jasper formations in the Baton Rouge area due to prolonged groundwater withdrawals. Low-permeability faults are inferred by significant groundwater level difference across the faults. While local groundwater storage depletion in deeper aquifers is evident, overall estimated groundwater storage changes of the SHAS in the CAGCD are close to zero in the past two decades, indicating insignificant groundwater storage changes. This is attributed to dominant interactions between the major rivers and the shallower alluvial aquifer. In addition, the simulated groundwater storage changes exhibit patterns similar to those derived by the Gravity Recovery and Climate Experiment (GRACE) model that has been used in evaluation of groundwater depletion in many regional studies.

Hydrometeorological Conditions of the Volga Flow Generation into the Caspian Sea during the Last Glacial Maximum

The goal of this study is to evaluate annual and seasonal inflow from the Volga catchment area to the Caspian Sea during the Last Glacial Maximum (LGM ~21,000 years ago) using paleoclimate modeling data. The first approach is based on the LGM simulation by the general circulation models (GCMs) in the framework of the Paleoclimate Modelling Intercomparison Project (PMIP4) and the Coupled Modelling Intercomparison Project (CMIP6). We used four GCMs: INM-CM4-8, MIROC-ES2L, AWI-ESM1-1-LR, and MPI-ESM1-2-LR. The second approach is based on the spatially distributed process-based runoff generation model using PMIP4-CMIP6 model data as boundary conditions. The use of the hydrological ECOMAG model allows us to refine estimates of the Volga runoff in comparison to GCM calculations by considering seasonal features of runoff generation related to periglacial vegetation distribution, permafrost, and streamflow transformation along the channel network. The LGM is characterized by a high uncertainty in meteorological values calculated for the Volga basin using various GCMs. The share of runoff from the three most flooded months from the annual calculated in the LGM was 95%, according to INM-CM4-8, while other GCMs ranged from 69–78%. Three GCMs (MIROC-ES2L, AWI-ESM1-1-LR, and MPI-ESM1-2-LR) showed 83–88% of the present-day value of precipitation in the Volga basin during cooling for more than 10 °C, while INM-CM4-8 showed a two-fold decrease. According to hydrological modeling results using data from three models, the annual Volga runoff was significantly higher than the present-day value, and, when using data from INM-CM4-8, it was lower.

Potential of Satellite Rainfall Estimates as Inputs for Flood Forecasting: Case Study Gash River, Sudan

This study is an attempt to test the potential of using real time Satellite Rainfall Estimates (SRE) data for hydrological modeling. Tropical Rainfall Measurement Mission (TRMM-3B42RT V7) SRE was evaluated against observed rain gauge data in Gash river catchment. The TRMM was evaluated against intensity as well as elevation dependency. The Hydrologic Engineering Center’s Hydrologic Modeling System (HEC-HMS) software of the Army Corps of Engineering of the USA was used to simulate the rainfall - runoff process. The performance of TRMM was found to underestimate the rainfall for most of the events, the underestimation increases with the increase in elevation. TRMM data set was biased corrected and used as input to derive the hydrological model. Observed hydrographs at the catchment outlet were compared to the simulated flow hydrographs using events and continuous modeling. The results of hydrological modeling showed that events based modeling performed better, the coefficient of determination (R2) vary between 0.87 to 0.96 while Nash-Sutcliffe Efficiency (NSE), vary from 0.84 to 0.96 and Root Mean Square Error (RMSE) vary from 45 to 118.3 m3/s. While the same statistics for continuous modeling showed, (NSE = 0.65) and (RMSE 44.5 m3/s). These results reflect the high potential of TRMM data set as inputs for hydrological modeling and flood forecasting in the Gash river and other basins with similar characteristics.

Reconstructing the Historical Terrestrial Water Storage Variations in the Huang–Huai–Hai River Basin With Consideration of Water Withdrawals

The Huang-Huai-Hai River Basin in eastern China has suffered from severe water scarcity during recent decades due to the effects of climate change and human activities. Quantifying the changes in the amount of terrestrial freshwater available in this region and their driving factors is important for understanding hydrological processes and developing a sustainable water policy. This study proposed an ensemble learning model to reconstruct historical variations in the terrestrial water storage (TWS) of the Huang-Huai-Hai River Basin, China. The model was trained using the observations of the variations in TWS from the Gravity Recovery and Climate Experiment mission (GRACE) satellites, climatic driving, and human withdrawal datasets produced on a monthly scale. The variations in the reconstructed TWS were compared with the results of several land surface and hydrological models with a variety of in situ measurements of the soil water content. The contributions of the climate and human activity to the ensemble learning model were also quantified. The results show that the proposed approach generally outperforms the land surface and hydrological models examined in this study, matches the patterns in the GRACE solutions, and reconstructs past changes in TWS, which are consistent with the GRACE observations. Climatic variables are the most important in the ensemble learning model, with precipitation over the prior month being a critical factor. The model that includes human intervention tends to perform better than without it. Irrigation, industry, and domestic water withdrawals contribute equally to the model. This study provides a flexible and easily implementable model that can bridge the gap between GRACE observations and past changes in TWS. The model is applicable in areas with intense human activities, and the results have the potential to be assimilated into and enhance hydrological models.

Source Apportionment, Hydrodynamic Influence, and Environmental Stress of Pharmaceuticals in a Microtidal Estuary with Multiple Outlets in South China.

Pharmaceutical residues in the environment are of great concern as ubiquitous emerging contaminants. This study investigated the presence of 40 pharmaceuticals in water and sediment of the Pearl River Estuary (PRE) in the wet season of 2020. Among psychiatric drugs, only diazepam was found in water samples while six of them were detected in the sediment. The Σantibiotics levels ranged from 6.18 to 35.9 ng/L and 2.63 to 140 ng/g dry weight in water and sediment samples, respectively. Fluoroquinolones and tetracyclines were found well settling in the outlet sediment, while sulfonamides could be released from disturbed sediment under stronger tidal wash-out conditions. After entering the marine waters, pharmaceuticals tended to deposit at the PRE mouth by the influence of the plume bulge and onshore invasion of deep shelf waters. Low ecological risks to the aquatic organisms and of causing antimicrobial resistance were identified. Likewise, hydrological modeling results revealed insignificant risks: erythromycin-H2O and sulfamethoxazole discharged through the outlets constituted 30.8% and 6.74% of their environmental capacity, respectively. Source apportionment revealed that pharmaceutical discharges through the Humen and Yamen outlets were predominantly of animal origin. Overall, our findings provide strategic insights on environmental regulations to further minimize the environmental stress of pharmaceuticals in the PRE.

IMPROVEMENT OF THE TECHNIQUES FOR THE COMPUTATION OF EVAPOTRANSPIRATION MAPS

Abstract. The definition of a methodology for estimating suitable areas for new renewable sources needs to identify possible indexes that describe the fields. One interesting index is the Evapotranspiration (ET). This is usually determined from hydrological models, which however do not provide a real-time estimation. So, new different procedures have been tested to compute it, for example, the use of Satellite images. The Sen-ET project allows using the Copernicus data to compute daily evapotranspiration. An improvement in the automatization of methodology is presented within this work. The model is tested in two different Italian regions with different climates and geographical conditions, considering also different months to study the results in different seasons over the same area. Results are then compared with those obtained in the same areas and period with two hydrological models (BIGBANG and GlobWat). The different outputs are analysed according to their spatial resolutions and respect to agricultural land cover classes. The comparison between Sen-ET evapotranspiration maps and hydrological model results has shown, in the analysed areas, relevant differences and suggests interesting future insights for continuous monitoring of land subject to water stress.

Analysis of Changes in Land Use Patterns for Erosion and Sediment Prediction

The Amprong watershed has an area of ± 252.94 km². The increasing population causes the demand for land to increase. GIS analyzes changes with the factors of change that occur. The purpose of the study was to determine the pattern of land-use change in the Amprong watershed on erosion and sedimentation. Idrisi Selva for use change analysis and SWAT for erosion and sediment. From the results of land cover modeling, it can be seen that the trend of forest land cover change from 2008, 2013, and 2018 continued to decline with an average of 11.69% of land cover in 2008. In contrast to settlements, it always increased by an average of 9.29%. Meanwhile, land cover changes in 2018 and 2028 decreased by 21.40%, plantations decreased by 4.33%, open land decreased by 42.11%, settlements increased by 46.47%, rice fields increased by 0.60%, and shrubs increased by 4.63%. The Kappa Index of Agreement validation shows a K standard value of 0.85 which means that the modeling is scientifically accepted. The results of hydrological modeling in 2018 were 1,021,237.49 tons/ha/year, erosion was 752,973.95 tons/ha/year and in 2028 it was 1,886,689.64 tons/ha/year, erosion was 1,069,631.09 tons/year

Improved runoff forecasting performance through error predictions using a deep-learning approach

Accurate runoff prediction is critical for various fields of hydrology, agriculture, and environmental studies. Numerous hydrologic models have been developed and demonstrate good performances in runoff simulation. However, errors are inherent in forecasted runoff predictions, which can cause uncertainty in real-time flood warning systems. In order to improve the predictive performance of hydrologic modeling, this study used a deep learning approach as a post-processor to correct for errors associated with hydrologic data. The proposed model uses the long short-term memory model with sequence-to-sequence structure as a post-processor to improve runoff forecasting. Specifically, the deep learning approach was used to estimate errors in forecasted hourly runoff provided from National Water Model in Russian River basin, California, United States. Error prediction in hourly runoff with lead times between 1 and 18 h were developed using observed precipitation and errors from upstream stream gages to improve the predictive performance of National Water Model. The predictive performance of the model was evaluated using numerous statistical metrics, and results show that the long short-term memory model with sequence-to-sequence post-processor improved runoff predictions compared to standalone results from the National Water Model. Statistical values of percent bias decreased from a range of −60%–80% to −15%–10% when the post-processor model was used, and similarly root mean square errors of runoff prediction decreased from 120 cms to 20 cms. Thus, this study demonstrates the power of deep learning model to improve hydrologic modeling results, especially those with short forecasting lead times.

Оценка земель агрохозяйственных зон Томской области по результатам моделирования ступеней увлажнения

The scientific novelty of the relevance of this study is caused by the fact that for the effective development and use of space, an integral criterion is needed, calculated on the basis of spatially distributed data, characterizin the local hydrological and climatic conditions of growth, formed under the influence of landscape conditions. The aim of the work is to develop and verify a method for assessing the quality of land based on the results of hydrological and climatic modeling of the stages of humidification by L. G. Ramensky. The lands with moisture scale 55-70 were accepted as the most suitable for agroindustrial use. The studied territory, even within the same agricultural zone, is very heterogeneous, with a mosaic distribution of moisture levels. Unfavorable technological properties of land – high waterlogging, small-contour fields increase the cost of their processing, making agriculture in modern conditions low-profitable. For agricultural zones of the Tomsk region, according to the described method, the calculation of moisture scale was performed. A comparison of the estimates of land suitability based on the results of hydrological and climatic modeling of moisture scale with those previously obtained for agricultural zones of the Tomsk region showed good convergence for 8 zones out of 10. This allows us to consider the calculation of L. G. Ramensky's moisture scale as a working tool for optimizing land management. It should be noted that the use of the SRTM-height matrix, due to its inherent disadvantages, does not allow performing a fully adequate on-farm land assessment. It is necessary to use better digital terrain models. The received assessments of the quality of land resources confirm that high-quality land resources in the Tomsk region are very limited and appropriate efforts should be made to preserve them. In particular, the least productive arable land should be transferred to hayfields, and distant unused agricultural land to forest plantations with valuable tree species.

Effects of land-use and -cover changes on streamflow regime in the Brazilian Savannah

Study regionThe study was conducted in the Mortes River Basin (MRB), Mato Grosso State (Brazil), owing to the constant removal of native forest (Brazilian Savannah, popularly known as Cerrado) for agriculture and pasture purposes. Study focusThe hydrological model for large basins (MGB-IPH) was employed to assess the hydrological impacts due to land-use and land-cover scenarios. Three scenarios were built according to the soil use capacity in the MRB (C1: Savannah recovery and agriculture removal by pasture areas, C2: pasture expansion, and C3: agriculture expansion). New Hydrological insights for the regionIn general, the C2 and C3 results show an increase in water availability; however, C2 increases flood risk and C3 tends to worsen drought. These extreme configurations of land use and land cover reveal poor water distribution throughout the year. However, C1 provided the most favorable results, increasing water availability during the dry season without increasing flood risk. The results highlight that anthropic activities already dominate the upper, middle, and lower parts of the basin, representing a risk for sustainable land use. Our hydrological modeling results demonstrated strategies for planning and management to avoid critical scenarios of lack of water in the MRB due to the intensity of soil use beyond its capacity.

Assessment of Flood Risks in Ifo Local Government Area of Ogun State

The most frequent natural hazards and the most costly in terms of property damage in study area is Flood. The purpose of this work is use Geographic Information Systems (GIS) Hydrological tools to model and map flood event in Ifo Local Government Area of Ogun state, Nigeria using Shuttle Radar Topographic Mission ( SRTM) data and land use maps as input data layers. In this study, flood inundation depth is calculated with GIS in order to understand the temporal, spatial scale and transition. Flood inundation map and 3D views of the hydrological modelling results are provided for a better understanding of the severity of flooding in the location. It is recommended that the processed data from this study be used as input for future research and the development of flood warning systems for the area. It will also be a base study for future study when rainfall data will be available.

HYDROLOGICAL ASPECT OF BATANG ALAI WEIR WATERSHED DUE TO LAND USE CHANGES INTO MINING AREAS

The policy of the Ministry of Energy and Mineral Resources that provides permits for mining production activities for PT. MCM in the Batang Alai River area has an impact on the hydrology of the area. This study will discuss the changes in land use in the Batang Alai River area, Batang Alai Timur District, Hulu Sungai Tengah to the aspects of high flow and low flow hydrology. The research method used is manual calculation with rational methods for high flow hydrology and calculations using the ArcSWAT 2012.10.1.18 for low flow hydrology. The data needed in the processing are rainfall data, DEM data, daily discharge data, climatology data, land use data, land use change data, and Batang Alai watershed data. The results of the study were that the ratio of peak discharge of land use was changed to the mining area and land use was not converted into a mining area for high flow hydrology calculations. There is an increase in plan flood discharge in the Batang Alai River if there is a change in land use into a mining area, the design flood discharge always increases in each period. Whereas for low flow hydrology is the comparison between the daily use of land use converted into a mining area and land use is not converted into a mining area, then the daily discharge results are calculated for the availability of water. The results of low-flow hydrological modeling are categorized as good and have high influence based on the Nash-Sutcliffe efficiency value grouping and the coefficient of determination, and water availability decreases if land use is converted into a mining area.