Utilization and uncertainties of satellite precipitation data in flash flood hydrological analysis in ungauged watersheds

<p>Aim of the study is to examine the potential utilization of satellite precipitation data to estimate the peak discharges of flash floods in ungauged Mediterranean watersheds. Cumulative precipitation heights from local rain gauge and the GPM-IMERG were correlated in a scatter plot. The calculated linear equations were used to adjust the uncalibrated GPM-IMERG precipitation data in Thasos island (Northern Greece), to investigate the mechanisms of the flash floods recorded in November 2019 and to evaluate the significance of satellite precipitation data in hydrological modeling. The uncalibrated GPM-IMERG precipitation failed to explain the flash floods phenomena. The rain gauge data are reliable to accurately predict the peak discharges only in cases, where the rain gauges are within the study area. The strong correlation between ground rainfall data and satellite spatiotemporal precipitation data (R2 &gt; 0.65), provides linear regression equations that, through their extrapolation and appliance to the rest of the flooded area, could adjust and correct the satellite data, optimizing the efficiency and accuracy of flash flood analysis, especially in ungauged watersheds. The proposed methodology could highly contribute to the optimization of flood mitigation measures establishment, flood risk assessment, hydrological and hydraulic simulation of flash flood events in ungauged watersheds.</p>

Applying a Graphical Method in Evaluation of Empirical Methods for Estimating Time of Concentration in an Arid Region

At gauged watersheds, the time of concentration can be estimated using rainfall-runoff data; however, at ungauged watersheds, empirical methods are used instead. Large errors in the application of empirical methods may cause inaccurate modeling of floods and unreliable structure design. In this paper, methods for calculating the time of concentration (Tc) were compared to identify the best equation for estimating Tc in ungauged watersheds of an arid region. The graphical method, based on measured data, was compared to 15 empirical methods to determine which empirical method returned the best results. The graphical method was applied to 33 rainfall-runoff events in four rural sub-watersheds located in the central parts of Hormozgan province, Iran. A ranking-based procedure was used to select the best performing empirical methods. To minimize bias and improve accuracy, the best performing empirical methods were modified by adjusting their formulas. According to the study, three empirical methods: (1) Williams, (2) Pilgrim and Mac Dermott, and (3) Arizona DOT, performed the best in the study areas. The results also showed that the modified Williams and Arizona DOT’s formulas were able to estimate the time of concentration in ungauged watersheds with an error lower than 1%.

Spatial Recognition of Regional Maximum Floods in Ungauged Watersheds and Investigations of the Influence of Rainfall

This study primarily aims to develop a method for estimating the range of flood sizes in small and medium ungauged watersheds in local river streams. In practice, several water control projects have insufficient streamflow information. To compensate for the lack of data, the streamflow propagation method (SPM) provides streamflow information for ungauged watersheds. The ranges of flood sizes for ungauged watersheds were generated using a specific flood distribution analysis based on the obtained streamflow data. Furthermore, the influence of rainfall information was analyzed to characterize the patterns of specific flood distributions. Rainfall location, intensity, and duration highly affected the shape of the specific flood distribution. Concentrated rainfall locations affected the patterns of the maximum specific flood distribution. The shape and size of the minimum specific flood distribution were dependent on the rainfall intensity and duration. The Creager envelope curve was used to generate equations for the maximum/minimum specific flood distribution for the study site. The ranges of the specific flood distributions were produced for each watershed size.

Fuzzy clustering and distributed model for streamflow estimation in ungauged watersheds

This paper proposes a regionalization method for streamflow prediction in ungauged watersheds in the 7461 km2 area above the Gharehsoo Hydrometry Station in the Ardabil Province, in the north of Iran. First, the Fuzzy c-means clustering method (FCM) was used to divide 46 gauged (19) and ungauged (27) watersheds into homogenous groups based on a variety of topographical and climatic factors. After identifying the homogenous watersheds, the Soil and Water Assessment Tool (SWAT) was calibrated and validated using data from the gauged watersheds in each group. The calibrated parameters were then tested in another gauged watershed that we considered as a pseudo ungauged watershed in each group. Values of R-Squared and Nash–Sutcliffe efficiency (NSE) were both ≥ 0.70 during the calibration and validation phases; and ≥ 0.80 and ≥ 0.74, respectively, during the testing in the pseudo ungauged watersheds. Based on these metrics, the validated regional models demonstrated a satisfactory result for predicting streamflow in the ungauged watersheds within each group. These models are important for managing stream quantity and quality in the intensive agriculture study area.

Can snow persistence explain the spatial-temporal variabilities in streamflow hydrograph flashiness across snow-dominated regions?

&lt;p&gt;Recent research showed that, snow persistence, defined here as the fraction of time that snow is present on the ground, can play an important role in explaining spatial variability of average annual streamflow in moderately snowmelt-dominated regions. Here, we extend this work and explore the following questions: 1) whether globally available snow persistence data is useful for estimating a suite of streamflow signatures explaining the shape, flashiness and components of streamflow hydrograph, and 2) whether snow persistence could be useful for reconstructing streamflow patterns in ungauged watersheds, both spatially and temporally. We explore these questions across a spectrum of climatic dryness, snowiness, and geological settings. The motivations for the study are the need to understand how loss of snow may affect the components of streamflow in different climatic and geological settings, as well as the need for simple methods to predict components of streamflow in snow-dominated ungauged basins.&lt;/p&gt;

Application of Convolution Neural Networks and Hydrological Images for the Estimation of Pollutant Loads in Ungauged Watersheds

River monitoring and predicting analysis for establishing pollutant loads management require numerous budgets and human resources. However, it is general that the number of government officials in charge of these tasks is few. Although the government has been commissioning a study related to river management to experts, it has been inevitable to avoid the consumption of a massive budget because the characteristics of pollutant loads present various patterns according to topographic of the watershed, such as topology like South Korea. To address this, previous studies have used conceptual and empirical models and have recently used artificial neural network models. The conceptual model has a shortcoming in which it required massive data and has vexatious that has to enforce the sensitivity and uncertain analysis. The empirical model and artificial neural network (ANN) need lower data than a conceptual model; however, these models have a flaw that could not reflect the topographical characteristic. To this end, this study has used a convolution neural network (CNN), one of the deep learning algorithms, to reflect the topographical characteristic and had estimated the pollutant loads of ungauged watersheds. The estimation results for the biochemical oxygen demand (BOD) and total phosphorus (TP) loads for three ungauged watersheds were all excellent. However, prediction results with low accuracy were obtained when the hydrological images of a watershed with a land cover status different from the ungauged watersheds were used as training data for the CNN model.

Developing a Discharge Estimation Model for Ungauged Watershed Using CNN and Hydrological Image

This study aimed to estimate the discharge in ungauged watersheds. To this end, we herein deviated from the model development methodology of previous studies and used convolution neural network (CNN), a deep training algorithm, and hydrological images. As the CNN model was developed for solving classification issues in general, it is unsuitable for simulating the discharge, which is a continuous variable. Therefore, the fully connected layer of the CNN model was improved. Moreover, images reflecting the hydrological conditions rather than a general photograph were used as input data for the CNN model. Three study areas that have discharge gauged data were set for the model’s training and testing. The data from two of the three study areas were used for CNN model training, and the data of the other were used to evaluate model prediction performance. The results of this study demonstrate a moderate predictive success of the discharge of an ungauged watershed using the CNN model and hydrological images. Therefore, it can be suitable as a methodology for the discharge estimation of ungauged watersheds. Simultaneously, it is expected that our methodology can be applied to the field of remote sensing or to the field of real-time discharge simulation using satellite imagery on a global scale or across a wide area.