Hydrological modeling for streamflow and sediment yield simulation using the SWAT model in a forest-dominated watershed of north-eastern Himalayas of Kashmir Valley, India

In this study, the Soil and Water Assessment Tool (SWAT) model was used to examine the spatial variability of sediment yield, quantify runoff, and soil loss at the sub-basin level and prioritize sub-basins in the Sindh watershed due to its computational efficiency in complex watersheds. The Sequential Uncertainty Fitting-2 approach was used to determine the sensitivity and uncertainty of model parameters. The parameter sensitivity analysis showed that Soil Conservation Services Curve Number II is the most sensitive model parameter for streamflow simulation, whereas linear parameters for sediment re-entrainment is the most significant parameter for sediment yield simulation. This study used daily runoff and sediment event data from 2003 to 2013; data from 2003 to 2008 were utilized for calibration and data from 2009 to 2013 were used for validation. In general, the model performance statistics showed good agreement between observed and simulated values of streamflow and sediment yield for both calibration and validation periods. The noticed insights of this research show the ability of the SWAT model in simulating the hydrology of the Sindh watershed and its reliability to be utilized as a decision-making tool by decision-makers and researchers to influence strategies in the management of watershed processes.

Development and Application of a QGIS-Based Model to Estimate Monthly Streamflow

Changes in rainfall pattern and land use have caused considerable impacts on the hydrological behavior of watersheds; a Long-Term Hydrologic Impact Analysis (L-THIA) model has been used to simulate such variations. The L-THIA model defines curve number according to the land use and hydrological soil group before calculating the direct runoff based on the amount of rainfall, making it a convenient method of analysis. Recently, a method was proposed to estimate baseflow using this model, which may be used to estimate the overall streamflow. Given that this model considers the spatial distribution of land use and hydrological soil groups and must use rainfall data at multiple positions, it requires the usage of a geographical information system (GIS). Therefore, a model that estimates streamflow using land use maps, hydrologic soil group maps, and rain gauge station maps in QGIS, a popular GIS software, was developed. This model was tested in 15 watersheds.

Formulation of Parsimonious Urban Flash Flood Predictive Model with Inferential Statistics

The curve number (CN) rainfall–runoff model is widely adopted. However, it had been reported to repeatedly fail in consistently predicting runoff results worldwide. Unlike the existing antecedent moisture condition concept, this study preserved its parsimonious model structure for calibration according to different ground saturation conditions under guidance from inferential statistics. The existing CN model was not statistically significant without calibration. The calibrated model did not rely on the return period data and included rainfall depths less than 25.4 mm to formulate statistically significant urban runoff predictive models, and it derived CN directly. Contrarily, the linear regression runoff model and the asymptotic fitting method failed to model hydrological conditions when runoff coefficient was greater than 50%. Although the land-use and land cover remained the same throughout this study, the calculated CN value of this urban watershed increased from 93.35 to 96.50 as the watershed became more saturated. On average, a 3.4% increase in CN value would affect runoff by 44% (178,000 m3). This proves that the CN value cannot be selected according to the land-use and land cover of the watershed only. Urban flash flood modelling should be formulated with rainfall–runoff data pairs with a runoff coefficient > 50%.

A Daily Water Balance Model Based on the Distribution Function Unifying Probability Distributed Model and the SCS Curve Number Method

A new daily water balance model is developed and tested in this paper. The new model has a similar model structure to the existing probability distributed rainfall runoff models (PDM), such as HyMOD. However, the model utilizes a new distribution function for soil water storage capacity, which leads to the SCS (Soil Conservation Service) curve number (CN) method when the initial soil water storage is set to zero. Therefore, the developed model is a unification of the PDM and CN methods and is called the PDM–CN model in this paper. Besides runoff modeling, the calculation of daily evaporation in the model is also dependent on the distribution function, since the spatial variability of soil water storage affects the catchment-scale evaporation. The generated runoff is partitioned into direct runoff and groundwater recharge, which are then routed through quick and slow storage tanks, respectively. Total discharge is the summation of quick flow from the quick storage tank and base flow from the slow storage tank. The new model with 5 parameters is applied to 92 catchments for simulating daily streamflow and evaporation and compared with AWMB, SACRAMENTO, and SIMHYD models. The performance of the model is slightly better than HyMOD but is not better compared with the 14-parameter model (SACRAMENTO) in the calibration, and does not perform as well in the validation period as the 7-parameter model (SIMHYD) in some areas, based on the NSE values. The linkage between the PDM–CN model and long-term water balance model is also presented, and a two-parameter mean annual water balance equation is derived from the proposed PDM–CN model.

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

GIS-Based Surface Runoff Modeling Using Empirical Technique For A River Basin In South India

Precipitation is the primary source of fresh water in the world. Surface runoff will happen when the amount of rainfall is greater than the soil’s infiltration capacity. In most water resource applications, runoff is the most important hydrological variable. Aside from these rainfall characteristics, there are a number of catchment-specific elements that have a direct impact on runoff amount and volume. This research focuses on estimating surface runoff over the lower Vellar basin, a river basin in the southern part of India, by integrating Soil Conservation Service-Curve Number (SCS-CN) method with GIS. This technique is one of the most common methods used by hydrologists for estimating surface runoff. Curve Number (CN) is an index established by the Natural Resource Conservation Service (NRCS) to denote the potential for stormwater runoff. The nature of the watershed is explored first by creating land use and land cover pattern followed by the preparation of slope, drainage, and location maps. The area taken for this study is the lower Vellar basin situated in the Cuddalore District of Tamil Nadu, India. The curve number is analyzed using the rainfall data of 15 years (2001-2015) and the runoff is being calculated. The watershed pattern of the study area is also explored being analyzed and executed. Preservation of the runoff water is also discussed.

ASSESSING THE IMPACT OF URBANIZATION ON DIRECT RUNOFF IN DISTRICT 8, HO CHI MINH CITY

Ho Chi Minh City (HCMC) is among the cities that are most at risk of fl ooding worldwide. Urbanization processes have led to a change in land use, which in turn has resulted in an increase in impervious surfaces and runoff , thus again leading to the risk of fl ooding in the city. The aim of the article is to study the impact of urban development (on the example of District 8 of HCMC) on surface runoff using a combination of the interpretation of remote sensing images of the earth (ERS) Google Earth and the SCS-CN model (the Soil Conservation Service curve number). Theoretical models are used to analyze the relationship between the typology of buildings and areas of open and impervious surfaces. The interpretation of remote sensing images was carried out in the ArcGIS program. The method used to calculate surface runoff is the Soil Conservation Service Curve Number (SCS-CN) method developed by the US Soil Conservation Service and is suitable for assessing the eff ects of land-use/land cover change due to urbanization. The results of the study show the volume of surface runoff in areas with diff erent levels of urbanization in district 8 of HCMC, assessing the impact of urbanization processes on surface runoff and revealing new opportunities for managing this process. The combination of remote sensing interpretation and SCS-CN model makes it possible to assess the impact of urban development on surface runoff . Urbanization and an increase in built-up area strongly aff ect fl ooding, reducing the soil retention.

Water and sediment yield estimation: A case study in Bawashaswar Watershed/ Iraqi Kurdistan Region

Water flow in the seasonal streams forms a vital supplier of water resources, particularly in arid and semiarid areas due to its high water supply from the precipitation during the wet season. This research aims to estimate water and sediment yield in the Bawashaswar watershed (BW), which is far ~90 km from Kirkuk City and covers an area of 277 km2. The Bawashaswar Dam (BD) was constructed for multi-purposes, such as drinking, irrigation, livestock, and tourism at the outlet of the watershed north of Kifri town. The Soil Water Assessment Tool (SWAT) was integrated with several datasets, such as satellite images, Digital Elevation Model (DEM), land use, land cover, curve number (CN), daily climatic data of temperature, precipitation, and digital soil map to achieve the results. The estimated results include precipitation, evaporation transpiration, percolation, surface runoff, water yield, loss of transport, and sediment yield. The results showed that almost all the siltation (>0.5 ton/ha) comes from the western and northern parts of the BW due to the high topographic relief and the high channel discharge there. These results will support the future development plans and management of the watershed through mitigating and minimizing the size of the ground surface degradation problems all over the study area with increasing the lifespan of the BD.