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Stochastic and Deterministic Modelling of Hydrologic Variables

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A special issue of Hydrology (ISSN 2306-5338). This special issue belongs to the section "Statistical Hydrology".

Deadline for manuscript submissions: closed (31 March 2023) | Viewed by 39002

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Dr. Monzur Imteaz

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Department of Civil and Construction Engineering, School of Engineering, Swinburne University of Technology, Melbourne, Australia
Interests: water quality; water treatment; water recycling; water harvesting; modeling; forecasting
Special Issues, Collections and Topics in MDPI journals

Dr. Shahriar Shams

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Guest Editor

Faculty of Engineering, Universiti Teknologi Brunei, Jalan Tungku Link, Gadong BE1410, Brunei
Interests: water treatment; water pollution; climate change; IWRM; hydroinformatics

Dr. Amimul Ahsan

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Guest Editor

1. Department of Civil & Construction Engineering, Swinburne University of Technology, Melbourne, VIC 3122, Australia
2. Department of Civil and Environmental Engineering, Islamic University of Technology (IUT), Gazipur 1700, Bangladesh
Interests: water; wastewater; saline water; quality; treatment; waste; impact
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Global climate change will severely impact the traditional hydrological cycle, the impacts of which are already being observed in many parts of the world. Ever-increasing emissions of carbon dioxide and other greenhouse gas (GHG) are causing an increase in global average temperature. An increase in temperature will directly affect evaporation and rainfall, which subsequently will affect catchment runoff and groundwater storage. Climate scientists are currently providing several projection scenarios based on level of GHG emissions in future years. On one hand, different global bodies and authorities are trying to minimize the GHG emissions through different novel sustainable measures. However, the level of implementations is not the same across the globe, and many regions/countries are not capable of adopting higher-level sustainable features due to high initial costs. Even with very high implementations of sustainable features, it will not be possible to avert impending global warming due to very high concentrations of GHGs that are already accumulated in the atmosphere. As such, different authorities are investigating potential adaptation measures against consequences of climate adversities, almost of all of which are hydrological variables. As in-depth experimental works with such climate-related parameters are often very expensive, tedious and time consuming, the scientific community prefers to develop mathematical models to assess the consequences of different hydrological parameters on other dependent variables (i.e., output parameters), such as catchment runoff, flood level, evaporation, groundwater level, reservoir level, concentrations of different pollutants and many more. There are two major types of mathematical models: stochastic and deterministic. Both are necessary depending on the need, and with the advancement of computational power as well as underlying theories, such models are capable of providing accurate estimations of those dependent variables. Based on such estimations, authorities rely on formulating their strategic plans. This Special Issue of Hydrology will focus on such modelling efforts, which are likely to produce benefits for stakeholders dealing with the above-mentioned variables. The Special Issue will cover, but is not limited, to the following main themes, as well as related themes:

Statistical modelling of rainfall;
Long-term seasonal rainfall/streamflow forecasting;
Deterministic modelling for runoff;
Deterministic modelling of river/creek and ground water level;
Statistical modelling of runoff;
Statistical methods for catchment water quality estimations;
Statistical methods for river/stream water quality estimations;
Deterministic modelling of river/stream water quality estimations;
Deterministic models for reservoir water quality simulations;
Artificial intelligence and machine learning for all the above-mentioned topics.

Dr. Monzur A. Imteaz
Dr. Shahriar Shams
Dr. Amimul Ahsan
Guest Editors

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Keywords

hydrologic modelling
hydraulic modelling
rainfall/streamflow forecasting
hydroinformatics
climate change
water recycling and groundwater modelling

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Published Papers (11 papers)

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13 pages, 2983 KiB

Open AccessArticle

Are the Regional Precipitation and Temperature Series Correlated? Case Study from Dobrogea, Romania

by Alina Bărbulescu and Florin Postolache

Hydrology 2023, 10(5), 109; https://doi.org/10.3390/hydrology10050109 - 11 May 2023

Cited by 2 | Viewed by 2016

Abstract

In the context of climate change, this article tries to answer the question of whether a correlation exists between the precipitation and temperature series at a regional scale in Dobrogea, Romania. Six sets of time series are used for this aim, each of them containing ten series—precipitation and temperatures—recorded at the same period at the same hydro-meteorological stations. The existence of a monotonic trend was first assessed for each individual series. Then, the Regional time series (RTS) (one for a set of series) were built and the Mann–Kendall test was employed to test the existence of a monotonic trend for RTSs. In an affirmative case, Sen’s method was employed to determine the slope of the linear trend. Finally, nonparametric trend tests were utilized to verify if there was a correlation between the six RTSs. This study resulted in the fact that the only RTS presenting an increasing trend was that of minimum temperatures, and there was a weak correlation between the RTS of minimum precipitations and maximum temperatures. Full article

(This article belongs to the Special Issue Stochastic and Deterministic Modelling of Hydrologic Variables)

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32 pages, 13422 KiB

Open AccessArticle

ML-Based Streamflow Prediction in the Upper Colorado River Basin Using Climate Variables Time Series Data

by Pouya Hosseinzadeh, Ayman Nassar, Soukaina Filali Boubrahimi and Shah Muhammad Hamdi

Hydrology 2023, 10(2), 29; https://doi.org/10.3390/hydrology10020029 - 19 Jan 2023

Cited by 15 | Viewed by 4372

Abstract

Streamflow prediction plays a vital role in water resources planning in order to understand the dramatic change of climatic and hydrologic variables over different time scales. In this study, we used machine learning (ML)-based prediction models, including Random Forest Regression (RFR), Long Short-Term Memory (LSTM), Seasonal Auto- Regressive Integrated Moving Average (SARIMA), and Facebook Prophet (PROPHET) to predict 24 months ahead of natural streamflow at the Lees Ferry site located at the bottom part of the Upper Colorado River Basin (UCRB) of the US. Firstly, we used only historic streamflow data to predict 24 months ahead. Secondly, we considered meteorological components such as temperature and precipitation as additional features. We tested the models on a monthly test dataset spanning 6 years, where 24-month predictions were repeated 50 times to ensure the consistency of the results. Moreover, we performed a sensitivity analysis to identify our best-performing model. Later, we analyzed the effects of considering different span window sizes on the quality of predictions made by our best model. Finally, we applied our best-performing model, RFR, on two more rivers in different states in the UCRB to test the model’s generalizability. We evaluated the performance of the predictive models using multiple evaluation measures. The predictions in multivariate time-series models were found to be more accurate, with RMSE less than 0.84 mm per month, R-squared more than 0.8, and MAPE less than 0.25. Therefore, we conclude that the temperature and precipitation of the UCRB increases the accuracy of the predictions. Ultimately, we found that multivariate RFR performs the best among four models and is generalizable to other rivers in the UCRB. Full article

(This article belongs to the Special Issue Stochastic and Deterministic Modelling of Hydrologic Variables)

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16 pages, 6450 KiB

Open AccessEditor’s ChoiceArticle

Synthetic Drought Hydrograph

by Radu Drobot, Aurelian Florentin Draghia, Nicolai Sîrbu and Cristian Dinu

Hydrology 2023, 10(1), 10; https://doi.org/10.3390/hydrology10010010 - 30 Dec 2022

Viewed by 2818

Abstract

Droughts are natural disasters with a significant impact on the economy and social life. Prolonged droughts can cause even more damage than floods. The novelty of this work lies in the definition of a synthetic drought hydrograph (SDH) which can be derived at each gaging station of a river network. Based on drought hydrographs (DHs) recorded for a selected gaging station, the SDH is statistically characterized and provides valuable information to water managers regarding available water resources during the drought period. The following parameters of the registered drought hydrograph (DH) are proposed: minimum drought discharge

Q_{D}^{m i n}

, drought duration

D_{D}

and deficit volume

V_{D}

. All these parameters depend on the drought threshold

Q_{T}

, which is chosen based on either pure hydrological considerations or on socio-economic consequences. For the same statistical parameters of the drought, different shapes of the synthetic drought hydrograph (SDH) can be considered. In addition, the SDH varies according to the probabilities of exceedance of the minimum drought discharge and deficit volume. Full article

(This article belongs to the Special Issue Stochastic and Deterministic Modelling of Hydrologic Variables)

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18 pages, 4635 KiB

Open AccessArticle

Daily Streamflow Forecasting in Mountainous Catchment Using XGBoost, LightGBM and CatBoost

by Robert Szczepanek

Hydrology 2022, 9(12), 226; https://doi.org/10.3390/hydrology9120226 - 13 Dec 2022

Cited by 54 | Viewed by 6875

Abstract

Streamflow forecasting in mountainous catchments is and will continue to be one of the important hydrological tasks. In recent years machine learning models are increasingly used for such forecasts. A direct comparison of the use of the three gradient boosting models (XGBoost, LightGBM and CatBoost) to forecast daily streamflow in mountainous catchment is our main contribution. As predictors we use daily precipitation, runoff at upstream gauge station and two-day preceding observations. All three algorithms are simple to implement in Python, fast and robust. Compared to deep machine learning models (like LSTM), they allow for easy interpretation of the significance of predictors. All tested models achieved Nash-Sutcliffe model efficiency (NSE) in the range of 0.85–0.89 and RMSE in the range of 6.8–7.8 m

^{3}

^{- 1}

. A minimum of 12 years of training data series is required for such a result. The XGBoost did not turn out to be the best model for the daily streamflow forecast, although it is the most popular model. Using default model parameters, the best results were obtained with CatBoost. By optimizing the hyperparameters, the best forecast results were obtained by LightGBM. The differences between the model results are much smaller than the differences within the models themselves when suboptimal hyperparameters are used. Full article

(This article belongs to the Special Issue Stochastic and Deterministic Modelling of Hydrologic Variables)

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21 pages, 4733 KiB

Open AccessFeature PaperArticle

Hydro-Climate Variability and Trend Analysis in the Jemma Sub-Basin, Upper Blue Nile River, Ethiopia

by Kidist Hilemicael Gonfa, Tena Alamirew and Assefa M Melesse

Hydrology 2022, 9(12), 209; https://doi.org/10.3390/hydrology9120209 - 24 Nov 2022

Cited by 6 | Viewed by 2774

Abstract

Understanding hydro-climate variability in areas where communities are strongly dependent on subsistence natural resource-based economies at finer spatial resolution can have substantial benefits for effective agricultural water management. This study investigated the hydro-climate variability and trend of the Jemma sub-basin, in the Upper Blue Nile (Abbay) basin, using Mann–Kendall test, Sen’s slope estimator, and Standardized Precipitation Index (SPI). Climate data from 11 weather stations inside the basin and two major streams were used for the statistical analysis. The climate data were also correlated with the ENSO phenomenon to explain drivers of the variability. The results show that the sub-basin has been experiencing normal to moderate variability in the annual and Kiremt season rainfalls, but high variability and declining trend for 73% of the minor (Belg) season rainfall, negatively affecting the planting of short-cycle crops that account for about 20% of crop production in the study area. Generally, strong El Nińo (SST anomaly >1) has been correlated to a substantial decline in the Belg season rainfall. Stream-flow variability has also been found to be very high (CV > 30%) in both river flow monitoring stations. Subsequently, ensuring agricultural water security for short-cycle crop production seems to be a risky and daunting task unless supplemented with groundwater conjunctive use or water harvesting. Full article

(This article belongs to the Special Issue Stochastic and Deterministic Modelling of Hydrologic Variables)

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17 pages, 15965 KiB

Open AccessArticle

On the Benefits of Bias Correction Techniques for Streamflow Simulation in Complex Terrain Catchments: A Case-Study for the Chitral River Basin in Pakistan

by Muhammad Usman, Rodrigo Manzanas, Christopher E. Ndehedehe, Burhan Ahmad, Oluwafemi E. Adeyeri and Cornelius Dudzai

Hydrology 2022, 9(11), 188; https://doi.org/10.3390/hydrology9110188 - 24 Oct 2022

Cited by 6 | Viewed by 2812

Abstract

This work evaluates the suitability of linear scaling (LS) and empirical quantile mapping (EQM) bias correction methods to generate present and future hydrometeorological variables (precipitation, temperature, and streamflow) over the Chitral River Basin, in the Hindukush region of Pakistan. In particular, LS and EQM are applied to correct the high-resolution statistically downscaled dataset, NEX-GDDP, which comprises 21 state-of-the-art general circulation models (GCMs) from the coupled model intercomparison project phase 5 (CMIP5). Raw and bias-corrected NEX-GDDP simulations are used to force the (previously calibrated and validated) HBV-light hydrological model to generate long-term (up to 2100) streamflow projections over the catchment. Our results indicate that using the raw NEX-GDDP leads to substantial errors (as compared to observations) in the mean and extreme streamflow regimes. Nevertheless, the application of LS and EQM solves these problems, yielding much more realistic and plausible streamflow projections for the XXI century. Full article

(This article belongs to the Special Issue Stochastic and Deterministic Modelling of Hydrologic Variables)

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19 pages, 8944 KiB

Open AccessEditor’s ChoiceArticle

Suitability Assessment of Fish Habitat in a Data-Scarce River

by Aysha Akter, Md. Redwoan Toukir and Ahammed Dayem

Hydrology 2022, 9(10), 173; https://doi.org/10.3390/hydrology9100173 - 3 Oct 2022

Cited by 2 | Viewed by 2964

Abstract

Assessing fish habitat suitability in a data-scarce tidal river is often challenging due to the absence of continuous water quantity and quality records. This study is comprised of an intensive field study on a 42 km reach which recorded bathymetry and physical water quality parameters (pH, electroconductivity, dissolved oxygen, and total dissolved solids) testing and corresponding water levels and velocity. Frequent water sampling was carried out on 17 out of 90 locations for laboratory water quality tests. Based on this, an interpolation technique, i.e., Inverse Distance Weighted (IDW), generates a map in a Geographic Information System (GIS) environment using ArcGIS software to determine the river water quality parameters. Additionally, a hydrodynamic model study was conducted to simulate hydraulic parameters using Delft3D software followed by a water quality distribution. During validation, the Delft3D-simulated water quality could reasonably mimic most field data, and GIS featured dissolved oxygen. The overall water quality distribution showed a lower dissolved oxygen level (~3 mg/L) in the industrial zone compared to the other two zones during the study period. On the other hand, these validated hydraulic properties were applied in the Physical Habitat Simulation Model (PHABSIM) set up to conduct the hydraulic habitat suitability for Labeo rohita (Rohu fish). Thus, the validated model could represent the details of habitat suitability in the studied river for future decision support systems, and this study envisaged applying it to other similar rivers. Full article

(This article belongs to the Special Issue Stochastic and Deterministic Modelling of Hydrologic Variables)

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15 pages, 2643 KiB

Open AccessArticle

Comparing Statistical Downscaling and Arithmetic Mean in Simulating CMIP6 Multi-Model Ensemble over Brunei

by Hamizah Rhymee, Shahriar Shams, Uditha Ratnayake and Ena Kartina Abdul Rahman

Hydrology 2022, 9(9), 161; https://doi.org/10.3390/hydrology9090161 - 8 Sep 2022

Cited by 9 | Viewed by 3040

Abstract

The climate is changing and its impacts on agriculture are a major concern worldwide. The impact of precipitation will influence crop yield and water management. Estimation of such impacts using inputs from the General Circulation Models (GCMs) for future years will therefore assist managers and policymakers. It is therefore important to evaluate GCMs on a local scale for an impact study. As a result, under the Shared Socioeconomic Pathways (SSPs) future climate scenarios, namely SSP245, SSP370, and SSP585, simulations of mean monthly and daily precipitation across Brunei Darussalam in Phase 6 of the Coupled Model Intercomparison Project (CMIP6) were evaluated. The performance of two multi-model ensemble (MME) methods is compared in this study: the basic Arithmetic Mean (AM) of MME and the statistical downscaling (SD) of MME utilizing multiple linear regression (MLR). All precipitation simulations are bias-corrected using linear scaling (LS), and their performance is validated using statistical metrics such as Root Mean Square Error (RMSE) and coefficient of determination (R²). The adjusted mean monthly precipitation during the validation period (2010–2019) shows an improvement, especially for the SD model with R² = 0.85, 0.86 and 0.84 for SSP245, SSP370 and SSP585, respectively. Although the two models produced unsatisfying results in producing annual precipitation. Future analysis under the SD model shows that there will be a much lower average monthly trend in comparison with the observed trend. On the other hand, the forecasted monthly precipitation under AM predicted the same rainfall trend as the baseline period in the far future. It is projected that the annual precipitation in the near future will be reduced by at least 27% and 11% under the SD and AM models, respectively. In the long term, less annual precipitation changes for the SD model (17%). While the AM model estimated a decrease in precipitation by at least 14%. Full article

(This article belongs to the Special Issue Stochastic and Deterministic Modelling of Hydrologic Variables)

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15 pages, 15126 KiB

Open AccessArticle

Maximum Extreme Flow Estimations in Historical Hydrological Series under the Influence of Decadal Variations

by Marco Antonio Jacomazzi, Antonio Carlos Zuffo, Monzur Alam Imteaz, Vassiliki Terezinha Galvão Boulomytis, Marcus Vinícius Galbetti and Tais Arriero Shinma

Hydrology 2022, 9(8), 130; https://doi.org/10.3390/hydrology9080130 - 25 Jul 2022

Cited by 2 | Viewed by 2600

Abstract

The hypothesis of stationarity is a fundamental condition for the application of the statistical theory of extreme values, especially for climate variables. Decadal-scale fluctuations commonly affect maximum and minimum river discharges. Thus, the probability estimates of extreme events need to be considered to enable the selection of most appropriate time series. The current study proposed a methodology to detect the fluctuation of long wet and dry periods. The study was carried out at the gauging station 4C-001 in Pardo River, State of São Paulo, Brazil. The Spearman, Mann–Kendall and Pettitt’s non-parametric tests were also performed to verify the existence of a temporal trend in the maximum annual daily flows. The graph achieved from the Pettitt’s statistical variable allowed for the identification and separation of the longest dry period (1941 to 1975) and the longest wet period (1976 to 2011), decreasing again in 2012. Analysing the series separately, it was observed that both mean and standard deviation were higher than those corresponding to the dry period. The probable maximum flows for the corrected series showed estimates 10% higher than those estimated for the uncorrected historical series. The proposed methodology provided more realistic estimates for the extreme maximum flows. Full article

(This article belongs to the Special Issue Stochastic and Deterministic Modelling of Hydrologic Variables)

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15 pages, 2382 KiB

Open AccessArticle

Monthly Rainfall Prediction at Catchment Level with the Facebook Prophet Model Using Observed and CMIP5 Decadal Data

by Md Monowar Hossain, A. H. M. Faisal Anwar, Nikhil Garg, Mahesh Prakash and Mohammed Bari

Hydrology 2022, 9(6), 111; https://doi.org/10.3390/hydrology9060111 - 17 Jun 2022

Cited by 8 | Viewed by 3597

Abstract

Early prediction of rainfall is important for the planning of agriculture, water infrastructure, and other socio-economic developments. The near-term prediction (e.g., 10 years) of hydrologic data is a recent development in GCM (General Circulation Model) simulations, e.g., the CMIP5 (Coupled Modelled Intercomparison Project Phase 5) decadal experiments. The prediction of monthly rainfall on a decadal time scale is an important step for catchment management. Previous studies have considered stochastic models using observed time series data only for rainfall prediction, but no studies have used GCM decadal data together with observed data at the catchment level. This study used the Facebook Prophet (FBP) model and six machine learning (ML) regression algorithms for the prediction of monthly rainfall on a decadal time scale for the Brisbane River catchment in Queensland, Australia. Monthly hindcast decadal precipitation data of eight GCMs (EC-EARTH MIROC4h, MRI-CGCM3, MPI-ESM-LR, MPI-ESM-MR, MIROC5, CanCM4, and CMCC-CM) were downloaded from the CMIP5 data portal, and the observed data were collected from the Australian Bureau of Meteorology. At first, the FBP model was used for predictions based on: (i) the observed data only; and (ii) a combination of observed and CMIP5 decadal data. In the next step, predictions were performed through ML regressions where CMIP5 decadal data were used as features and corresponding observed data were used as target variables. The prediction skills were assessed through several skill tests, including Pearson Correlation Coefficient (PCC), Anomaly Correlation Coefficient (ACC), Index of Agreement (IA), and Mean Absolute Error (MAE). Upon comparing the skills, this study found that predictions based on a combination of observed and CMIP5 decadal data through the FBP model provided better skills than the predictions based on the observed data only. The optimal performance of the FBP model, especially for the dry periods, was mainly due to its multiplicative seasonality function. Full article

(This article belongs to the Special Issue Stochastic and Deterministic Modelling of Hydrologic Variables)

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23 pages, 2607 KiB

Open AccessPerspective

Past, Present, and Future of Using Neuro-Fuzzy Systems for Hydrological Modeling and Forecasting

by Yik Kang Ang, Amin Talei, Izni Zahidi and Ali Rashidi

Hydrology 2023, 10(2), 36; https://doi.org/10.3390/hydrology10020036 - 26 Jan 2023

Cited by 3 | Viewed by 2732

Abstract

Neuro-fuzzy systems (NFS), as part of artificial intelligence (AI) techniques, have become popular in modeling and forecasting applications in many fields in the past few decades. NFS are powerful tools for mapping complex associations between inputs and outputs by learning from available data. Therefore, such techniques have been found helpful for hydrological modeling and forecasting, including rainfall–runoff modeling, flood forecasting, rainfall prediction, water quality modeling, etc. Their performance has been compared with physically based models and data-driven techniques (e.g., regression-based methods, artificial neural networks, etc.), where NFS have been reported to be comparable, if not superior, to other models. Despite successful applications and increasing popularity, the development of NFS models is still challenging due to a number of limitations. This study reviews different types of NFS algorithms and discusses the typical challenges in developing NFS-based hydrological models. The challenges in developing NFS models are categorized under six topics: data pre-processing, input selection, training data selection, adaptability, interpretability, and model parameter optimization. At last, future directions for enhancing NFS models are discussed. This review–prospective article gives a helpful overview of the suitability of NFS techniques for various applications in hydrological modeling and forecasting while identifying research gaps for future studies in this area. Full article

(This article belongs to the Special Issue Stochastic and Deterministic Modelling of Hydrologic Variables)

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