Prediction of Runoff in Watersheds Located within Data-Scarce Regions

Abstract

The interest in the use of mathematical models for the simulation of hydrological processes has largely increased especially in the prediction of runoff. It is the subject of extreme research among engineers and hydrologists. This study attempts to develop a simple conceptual model that reflects the features of the arid environment where the availability of hydrological data is scarce. The model simulates an hourly streamflow hydrograph and the peak flow rate for any given storm. Hourly rainfall, potential evapotranspiration, and streamflow record are the significant input prerequisites for this model. The proposed model applied two (2) different hydrologic routing techniques: the time area curve method (wetted area of the catchment) and the Muskingum method (catchment main channel). The model was calibrated and analyzed based on the data collected from arid catchment in the center of Jordan. The model performance was evaluated via goodness of fit. The simulation of the proposed model fits both (a) observed and simulated streamflow and (b) observed and simulated peak flow rate. The model has the potential to be used for peak discharges’ prediction during a storm period. The modeling approach described in this study has to be tested in additional catchments with appropriate data length in order to attain reliable model parameters.

Assessment of the Streamflow and Evapotranspiration at Wabiga Juba Basin Using a Water Evaluation and Planning (WEAP) Model

“…The Ankara River Basin study conducted by [53], which has a 4932 km 2 drainage area, a forecasting sediment yield and streamflow using the SWAT model between 1989 and 1996 showed that the NSE value equals to 0.79 on A monthly basis for their simulated years. Similar research was conducted in the 5649 km 2 Lower Porsuk River Basin to estimate streamflow on a monthly basis using the SWAT model [51]. They found the calibration values of the NSE were 0.74 and 0.87 for the validation period, respectively.…”

“…A common statistic called Nash-Sutcliffe efficiency (NSE) is used to assess how much the residual difference deviates from the variance of the observed data. (Equation ( 1)) [2], [3].…”

“…PET is generally higher than AET because it reflects the maximum water amount that could be evaporated and transpired with unlimited access to water (also known as theoretical maximum water loss). PET does not account for the limitations from water availability (e.g., soil moisture content, soil salinity, groundwater levels, or rainfall patterns) [38][39][40], vegetation and plant physiology (e.g., plant stress, leaf area index, or stomatal resistance) [37], environmental (e.g., land surface characteristics, such as vegetation cover, soil type, and slope) [38], and climatic (e.g., temperature, humidity, solar radiation, and wind speed) [2] factors. However, there can be situations where AET exceeds PET.…”

“…Water shortages are a significant drawback to national socioeconomic development [1,2]. There is a need to find a solution to water reservation and allocation disputes in developing and developed countries.…”

Monthly Water Balance of Ungauged Watersheds Using Empirical and Conceptual Models: A Case Study of the Semiarid Mountainous Watersheds, Southwest of Saudi Arabia

“…Most continuous-time rainfall-runoff models come under the conceptual model category [8]. More focus has been placed on the use of conceptual models generally, which primarily benefits from black-box modeling and a calibration process to obtain hydrological parameters and streamflow [9,10] and to simulate groundwater levels [11,12]. The rainfall-runoff process at the catchment scale can be reliably predicted using distributed and conceptual models.…”