Assessing the Performance of a Hydrological Tank Model at Various Spatial Scales

Abstract

In this study we investigated the performance of the Tank hydrologic model in predicting rainfall-runoff using a descriptive-analytical approach, including objective error measures and flow signatures (mean annual specific runoff, mean flow duration curves, normalized low flow and high flow indexes, coefficient of variation, and hydrograph flashiness). Because hydrological processes vary at different scales, three watersheds located in three significantly different environments, which are examples of small (15 km 2), medium (215 km 2) and large (542 km 2) watersheds, were selected to evaluate the performance of the hydrologic model. We found that Neka (the small watershed) has a large water budget with a steep flow slope, including both extremely low flow (lowest) and high flow (highest), a very high variation, and a flashy hydrograph (0.21 ≤ HF ≤ 0.41). Ghezel-Ozan (the medium watershed) also has a large water budget with a fairly steep flow slope, an extremely low flow and a moderate high flow, a moderate variation, and a very steady hydrograph (0.01 ≤ HF ≤0.02). Babolroud (the large watershed) possesses a moderate water budget with a moderate flow slope, a below-average low flow, a moderate high flow, an average variation, and a fairly flashy hydrograph (0.22 ≤ HF ≤ 0.28). The quality of rainfall-runoff modeling was good to satisfactory for the medium and large watersheds as shown by the two error metrics (1.32 ≤ RMSE ≤ 2.58, 0.61 ≤ NSE ≤ 0.8). However, the small watershed performance, especially in the validation period, was unsatisfactory (4.66 ≤ RMSE ≤ 5.33, 0.21 ≤ NSE ≤ 0.51). The limitations identified should be considered for medium watersheds. The Tank model is highly recommended for use in hydrologic predictions of large watersheds.

Groundwater nutrient loading into the northern Indian River lagoon: measurements and modeling

“…As a result, a FTR implies sufficient calibration, whereas R implies poor calibration. A perfect model would have an NSE = 1; however, the general descriptors used for model calibration based on NSE are NSE< 0.5 (unsatisfactory), 0.5< NSE<0.7 (satisfactory), and NSE > 0.7 (good) (Goodarzi et al, 2020). While the primary goal of using RMSE in evaluating model calibration is to minimize the numerical value of this statistical measure, there is no definitive RMSE threshold that can definitively designate a prediction as good or poor.…”

Integrated Hydrological Modeling to Analyze the Effects of Precipitation on Surface Water and Groundwater Hydrologic Processes in a Small Watershed

“…For example, 2 km grid rainfall was suggested by Bell and Moore [41] to model a small watershed (i.e., 132 km 2 in size). A small watershed may have a steep flow slope (flow change faster), high flow variations, and flashy hydrographs compared to a moderate and large watershed that may cause unsatisfactory model performance [46]. Runoff estimation improves as the watershed size increases, despite low rainfall resolution data [40,84].…”

“…The impacts vary significantly depending on the type of precipitation, hydrologic models, and watershed characteristics [15,16,21,28,31,33,35,[40][41][42][43][44][45]. For example, the sensitivity of spatial precipitation distribution to the surface-runoff response depends on the model scale [42,46]. Lopes [47] showed that spatial distribution of precipitation had significant effects on the runoff mechanism, irrespective of scales in the Walnut Gulch watershed, Arizona.…”

Performance of rainfall–runoff models in reproducing hydrological extremes: a case of the River Malaba sub-catchment

“…com/ site/ conyu tha/ tools-to-downl oad (accessed: 10 February 2021). These models were selected because they (1) are freely available online and (2) were found to be robust for rainfall-runoff modelling under various climatic conditions as demonstrated in several recent studies [27,36,[65][66][67][68][69].…”