Comparison of Rainfall-Runoff Simulation between Support Vector Regression and HEC-HMS for a Rural Watershed in Taiwan

To better understand the effect and constraint of different data lengths on the data-driven model training for the rainfall-runoff simulation, the support vector regression (SVR) approach was applied to the data-driven model as the core algorithm in the present study. Various features selection strategies and different data lengths were employed in the training phase of the model. The validated results of the SVR were compared with the rainfall-runoff simulation derived from a physically based hydrologic model, the Hydrologic Modeling System (HEC-HMS). The HEC-HMS was considered a conventional approach and was also calibrated with a dataset period identical to the SVR. Our results showed that the SVR and HEC-HMS models could be adopted for short and long periods of rainfall-runoff simulation. However, the SVR model estimated the rainfall-runoff relationship reasonably well even if the observational data of one year or one typhoon event was used. In contrast, the HEC-HMS model needed more parameter optimization and inference processes to achieve the same performance level as the SVR model. Overall, the SVR model was superior to the HEC-HMS model in the performance of the rainfall-runoff simulation.

Assessment of Variations in Runoff Due to Landcover Changes Using the SWAT Model in an Urban River in Dublin, Ireland

Investigating the impact of land cover change in hydrological modelling is essential for water resources management. This paper investigates the importance of landcover change in the development of a physically-based hydrological model called SWAT. The study area considered is the Dodder River basin located in southern Dublin, Ireland. Runoff at the basin outlet was simulated using SWAT for 1993–2019 using five landcover maps obtained for 1990, 2000, 2006, 2012 and 2018. Results indicate that, in general, the SWAT model-simulated runoff for a chosen time-period are closer to the real-world observations when the landcover data used for simulation was collated as close to the time-period for which the simulations were performed. For 23 (20) years (from 27 years period) the monthly mean (maximum) runoff for the Dodder River generated by the SWAT model had the least error when the nearby landcover data were used. This study indicates the necessity of considering dynamic and time-varying landcover data during the development of hydrological modelling for runoff simulation. Furthermore, two composite quantile functions were generated by using a kappa distribution for monthly mean runoff and GEV distribution for monthly maximum runoff, based on model simulations obtained using different landcover data corresponding to different time-period. Modelling landcover change patterns and development of projected landcover in the future for river basins in Ireland needs to be integrated with SWAT to simulate future runoff.

Impact of Climate Change on Runoff Prediction in Ogbese River Watershed

Increased rainfall amounts are projected in the humid southern parts of Nigeria due to climate change. The consequence of higher rainfall in future years would result to higher peak runoffs and flood stages in streams in these parts. The focus of this study is to simulate peak runoff at the outlet of Ogbese river watershed for future years of 2030, 2040, 2050 and 2060. Local twenty years (2000-2019) historical rainfall depths were used to statically downscale General Circulation Model outputs in the future for RCP 4.5 climate scenario. Downscaled rainfall depths were inputted in HEC-HMS model version 4.2 for rainfall-runoff simulation. The watershed was delineated with DEM in ArcGIS while four land use and land cover classifications were extracted with QGIS. Maximum rainfall depths projected in years 2030, 2040, 2050 and 2060 were 38.5mm/hr, 39mm/hr, 42mm/hr and 46mm/hr respectively. Peak runoff discharge simulated for RCP 4.5 climate scenario in years 2030, 2040, 2050 and 2060 are 1771m3/s, 1826 m3/s, 1897 m3/s and 2200 m3/s respectively. This represents 24.2% increase peak discharge between 2030 and 2060. Land area delineated for the catchment is 1946.2 km2. The LULC classification areas for urban area, forest, rock outcrop and bare land are 81.59 km2, 1721.84 km2, 146.27 km2 and 4.11 km2 respectively. The soil types are sandy clay loam (92.51 %), sandy loam (6.84 %), and clay (0.65 %). Curve Number and Initial abstraction parameter values are 70.27 and 2.89 respectively. Keywords- Climate change, GCM, HEC-HMS , Ogbese river, Peak runoff

A Novel Stacked Long Short-Term Memory Approach of Deep Learning for Streamflow Simulation

Rainfall-Runoff simulation is the backbone of all hydrological and climate change studies. This study proposes a novel stochastic model for daily rainfall-runoff simulation called Stacked Long Short-Term Memory (SLSTM) relying on machine learning technology. The SLSTM model utilizes only the rainfall-runoff data in its modelling approach and the hydrology system is deemed a blackbox. Conversely, the distributed and physically-based hydrological models, e.g., SWAT (Soil and Water Assessment Tool) preserve the physical aspect of hydrological variables and their inter-relations while taking a wide range of data. The two model types provide specific applications that interest modelers, who can apply them according to their project specification and objectives. However, sparse distribution of point-data may hinder physical models’ performance, which may not be the case in data-driven models. This study proposes a specific SLSTM model and investigates the SLSTM and SWAT models’ data dependency in terms of their spatial distribution. The study was conducted in the two distinct river basins of Samarahan and Trusan, Malaysia, with over 20 years of hydro-climate data. The Trusan basin’s rain gauges are scattered downstream of the basin outlet and Samarahan’s are located around the basin, with one station within each basin’s limits. The SWAT was developed and calibrated following its general modelling approach, however, the SLSTM performance was also tested using data preprocessing with principal component analysis (PCA). Results showed that the SWAT performance for daily streamflow simulation at Samarahan has been superior to that of Trusan. Both the SLSTM and PCA-SLSTM models, however, showed better performance at Trusan with PCA-SLSTM outperforming the SLSTM. This demonstrates that the SWAT model is greatly affected by the spatial distribution of its input data, while data-driven models, irrespective of the spatial distribution of their entry data, can perform well if the data adequacy condition is met. However, considering the structural difference between the two models, each has its specific application in a water resources context. The study of catchments’ response to changes in the hydrology cycle requires a physically-based model like SWAT with proper spatial and temporal distribution of its entry data. However, the study of a specific phenomenon without considering the underlying processes can be done using data-driven models like SLSTM, where improper spatial distribution of data cannot be a restricting factor.

Evaluation of Multiple Satellite-Based Precipitation Products for the Rainfall-Runoff Simulation over a Typical Catchment of Southwest China

Five non-real time satellite-based precipitation products (SPPs), including TMPA 3B42V7, CMORPH CRT, PERSIANN-CDR, GSMaP_MVK and GSMaP_Gauge, were evaluated over the Xijiang Basin. By driving XAJ model with each of the SPPs and gauge-based interpolation precipitation data to compare the hydrological responses at Wuzhou Station during the period of 2010–2017, this study also evaluated the applicability of these SPPs in rainfall-runoff simulation over the Xijaing Basin. The results showed that: (1) GSMaP_Gauge had highest accuracy, then are CMORPH CRT and TMPA 3B42V7, respectively, and finally are PERSIANN-CDR and GSMaP_MVK; (2) Among the five SPPs, CMORPH CRT, GSMaP_Gauge and TMPA-3B42 V7 have comparable performance in rainfall-runoff simulation, with NSE value lower than that generated by driving gauge-based interpolation precipitation and obviously higher than that of PERSIANN-CDR, and the uncorrected SPP, i.e., GSMaP_MVK, performs worst because of large systematic errors.

Impact of Fully Coupled Hydrology-Atmosphere Processes on Atmosphere Conditions: Investigating the Performance of the WRF-Hydro Model in the Three River Source Region on the Tibetan Plateau, China

The newly developed WRF-Hydro model is a fully coupled atmospheric and hydrological processes model suitable for studying the intertwined atmospheric hydrological processes. This study utilizes the WRF-Hydro system on the Three-River source region. The Nash-Sutcliffe efficiency for the runoff simulation is 0.55 compared against the observed daily discharge amount of three stations. The coupled WRF-Hydro simulations are better than WRF in terms of six ground meteorological elements and turbulent heat flux, compared to the data from 14 meteorological stations located in the plateau residential area and two flux stations located around the lake. Although WRF-Hydro overestimates soil moisture, higher anomaly correlation coefficient scores (0.955 versus 0.941) were achieved. The time series of the basin average demonstrates that the hydrological module of WRF-hydro functions during the unfrozen period. The rainfall intensity and frequency simulated by WRF-Hydro are closer to global precipitation mission (GPM) data, attributed to higher convective available potential energy (CAPE) simulated by WRF-Hydro. The results emphasized the necessity of a fully coupled atmospheric-hydrological model when investigating land-atmosphere interactions on a complex topography and hydrology region.

Model Calibration Parameter Using Optimization Trial in HEC-HMS for Unda Watershed

In water resource planning, information on water availability is needed. Nowadays, data on water availability is still difficult to obtain. With technology in the form of a rainfall-runoff simulation model that can predict water availability in the Unda watershed. It can add information about the potential for water in the Unda watershed. It can be used to prepare water resources management in the Unda watershed so that the existing potential can be used sustainably. Based on the rainfall simulation model results in the Unda watershed, it can be concluded that after running the initial model and calibration. The results are obtained R2 value was 0.68 and increased by 9.81% to 0.754. Both the initial model and the calibration model show an efficient R2 value, NASH value increases by 49.93% to 0.713, which includes satisfactory criteria, RMSE value of 1.135 and decreased by 49.47% to 0.758, and the PBIAS value was 44.70% which was classified as unsatisfactory and decreased from 80.24% to 24.80% at the time of calibration which was classified as satisfactory. In general, the overall simulation results are quite good for representing the watershed’s efficient hydrological process.