Effects of the Karkheh Dam construction on haze generation due to geomorphological changes (in the Khuzestan Province, Southwest of Iran)

An important factor for occurrence of dust storms is the construction of the Karkheh Dam in the Khuzestan province of Iran. It has reduced the annual mean of flow discharge in the Karkheh River from 120 to 50 m3/s and dried lands around river. The area of dried lands is 90.17 km2 around river and 333.45 km2 in the Hawr-al-Azim wetland. The Rosgen method, Fluvial-12 software, Shulits equation showed instability of the plan, cross sections of river and longitudinal slope of river, respectively, around Pay-e-pol hydrometric station (the upstream of river). After dam construction, extreme erosion occurred in this part of river. The type of sediment is clay and silt with D50 = 8 μm. The eroded sediment settles in downstream of river (around Hamidiyeh hydrometric station) and the Hawr-al-Azim wetland. The wind can easily lift these particles especially from May to July. Because of size of these particles, the haze concentration increased from 25% to 45% in dust storms. After construction dam, the dust storm days increased to 90 days in 2008. By increasing the stability of the river, the dust storms reduced from 2011. The annual volume of generated haze by geomorphological characteristic changes is almost 3107 m3.

Detection of abrupt shift and non-parametric analyses of trends in runoff time series in Dez River Basin

The present study aims to investigate the homogeneity of runoff time series and also to review the existence of trends in Tale Zang hydrometric station (the hydrometric station in the entrance of Dez Reservoir) runoff, using 61 years (1956–2016) daily observatory data. Pettit test, which is a common method in investigating the homogeneity of time series, was used to identify change points. Both Mann-Kendall and auto-correlated Mann-Kendall tests were applied to analyze the existence or non-existence of trends in each annual, seasonal, and monthly time series observed in a runoff. In time series, significant trends in 95% level of confidence were recognized, upper and lower limit values were presented for Sen's slope and it was tested for the increasing or decreasing trends in nature. Based on the results of this study, the significant change point in 95% level of confidence was recognized in Annual, Spring, Summer, Autumn, March, May, June, July, August, September, and October in 1997,1997, 1999, 1997, 1999, 1999, 1999, 1997, 2000, 2007, 2008, respectively. Analyzing the existence of a trend in 95% level of confidence indicated that in Spring, and in March, September, and October, for both Mann-Kendall and auto-correlated Mann-Kendall tests, the trend is significant and additive until the change point.

Monitoring of Yangtze River Discharge at Datong Hydrometric Station Using Acoustic Tomography Technology

To continuously monitor the discharge of the Yangtze River, two coastal acoustic tomography (CAT) systems with synchronized transmission were deployed at the Datong hydrometric station of the Yangtze River from July 2018 to January 2021. To accurately estimate the discharge of the Yangtze River, the cross-sectional averaged flow velocity and area data were estimated by establishing two empirical relationships: one between the range-averaged flow velocity measured by acoustic Doppler current profiler (ADCP) and the reciprocal travel time difference measured by CAT, and the other between the ADCP-measured cross-sectional area and the water-surface elevation (stage). Compared with the discharges directly measured by ADCP, our estimation had the root mean square error of 946 m3/s, accounting for 2.5% of the mean discharge. The discharges varied from 10,981 to 81,807 m3/s over the 2.5-years observational period, with a mean of 30,708 m3/s. The annual mean discharge was 29,163 and 34,763 m3/s in 2019 and 2020, respectively. Our monitoring successfully covered two complete flood processes, with a peak discharge of 69,744 (July 17, 2019) and 81,807 m3/s (July 13, 2020). Our study provides an innovative method to achieve accuracy and real-time monitoring of river discharges even during extreme flood events.

Recent Changes in Hydroclimatic Patterns over Medium Niger River Basins at the Origin of the 2020 Flood in Niamey (Niger)

Niamey, the capital of Niger, is particularly prone to floods, since it is on the banks of the Niger River, which in its middle basin has two flood peaks: one in summer (the red flood) and one in winter (the black flood). In 2020, the Niger River in Niamey reached its all-time highest levels following an abundant rainy season. On the other hand, the floods in Niamey have been particularly frequent in the last decade, a symptom of a change in hydroclimatic behaviour already observed since the end of the great droughts of the 1970s and 1980s and which is identified with the name of Sahelian Paradox. This study, starting from the analysis of the 2020 flood and from the update of the rating curve of the Niamey hydrometric station, analyses the rainfall–runoff relationship on the Sahelian basins of the Medium Niger River Basin (MNRB) that are at the origin of the local flood. The comparative analysis of runoffs, annual maximum flows (AMAX) and runoff coefficients with various rainfall indices calculated on gridded datasets allowed to hydroclimatically characterise the last decade as a different period from the wet one before the drought, the dry one and the post-drought one. Compared to the last one, the current period is characterised by a sustained increase in hydrological indicators (AMAX +27%) consistent with the increase in both the accumulation of precipitation (+11%) and the number (+51%) and magnitude (+54%) of extreme events in the MNRB. Furthermore, a greater concentration of rainfall and extremes (+78%) in August contributes to reinforcing the red flood’s positive anomalies (+2.23 st.dev in 2020). The study indicates that under these conditions the frequency of extreme hydrological events in Niamey will tend to increase further also because of the concurrence of drivers such as river-bed silting and levee effects. Consequently, the study concludes with the need for a comprehensive flood-risk assessment on the Niamey city that considers both recent hydroclimatic trends and urbanisation dynamics in flood zones hence defining the most appropriate risk-reduction strategies.

Probabilistic Assessment of Monthly River Flow Discharge Using Copula And OSVR Approaches

Simulation of flow discharge based on monthly precipitation values as inputs is one of the important issues in hydrology and water resources studies, especially in areas where data with the shorter time scales are not available. In this study, the applicability of support vector regression (SVR) model optimized by Ant colony and Copula-GARCH algorithms was investigated and compared to simulate the flow discharge based on total monthly rainfall in Talezang Basin, Iran. Entropy theory was used to select a suitable meteorological station corresponding to a hydrometric station. The vector autoregressive model was also used as the base model in Copula-GARCH simulations. The correlation results of the studied paired variable confirmed the possibility of using copula-based models. The simulation results were evaluated using R2, Nash-Sutcliffe Efficiency (NSE) and root mean square error (RMSE) statistics. According to the 99% confidence intervals of the simulations, the accuracy of both models was confirmed. The simulation results showed that the Copula-GARCH model was more accurate than the optimized SVR (OSVR) model. Considering the 90% efficiency (NSE = 0.90) of Copula-GARCH approach, the results show a 36% improvement of RMSE statistics by Copula-GARCH model compared to OSVR model in simulating the flow discharge on a monthly scale. The results also showed that by combining nonlinear ARCH models with the copula-based simulations, the reliability of the simulation results increases, which was also confirmed using the violin plot. The results also showed an increase in the accuracy of the Copula-GARCH model at the minimum and maximum values of the data.

Identification of Extreme Weather Events Using Meteorological and Hydrological Indicators in the Laborec River Catchment, Slovakia

This study used the standardized precipitation index (SPI) and the standardized runoff index (SRI) to analyze dry and humid conditions in the hill-country catchment area of the Laborec River (Slovakia) over a period of 50 years (1970–2019). Analysis of the SPI and SRI over various time scales showed the occurrence of wet periods (index > 1.0) that were associated with precipitation exceeding the long-term norm, and dry periods (index below −1.0), which were the result of small amounts of precipitation. Analysis of the correlation between the SPI and SRI on different time scales revealed that the catchment showed a weaker response to precipitation over short time scales (1 and 3 months) and a stronger response over longer accumulation periods (6, 9, and 12 months). The highest annual correlation coefficient (r = 0.72) was recorded between SRI-6 at the Humenne hydrometric station and SPI-9 at the Medzilaborce meteorological station in the upper part of the catchment area. The strongest annual correlation (r = 0.69) was obtained between the Izkovce and Kamenica stations in the lower part of the catchment area. As shown by the cross-relationships examined over different periods of accumulation of flows and precipitation, hydrological droughts appeared as a result of the occurrence of meteorological droughts with a three-month delay. The conducted analysis showed that in the case of the Laborec river catchment area, there was a strong correlation between the occurrence of meteorological drought and hydrological drought.

Hydrometeorological - hydrometric station network design using multicriteria decision analysis and GIS techniques

The design of an optimum hydrometeorological and hydrometric station network constitutes a key factor for the collection of comprehensive and reliable hydrometeorological and flow data that are necessary both for decision making in water resources policy and management and for the hydrometeorological risk assessment. This article describes a methodology developed in a geographic information system (GIS) assisted by a multicriteria decision making (MCDM) approach, which combines a set of spatial criteria in order to propose suitable locations for such a station network installation. Through the design for two networks that meet different requirements, various aspects concerning this methodology are illustrated, such as, the methods regarding criteria classification and weighting, as well as, the effect of weighting itself on the location rating. In particular, the implementation is performed for the Sarantapotamos river basin, an area located in the western part of Attica region, Greece, which is characterized by a great diversity of economic activities, mainly industrial, rural and urban fabric in the lowlands. Finally, the analysis indicates that, in terms of station density as proposed by the World Meteorological Organization (WMO), the optimum hydrometeorological station network consists of three stations and the hydrometric one of two stations.

Assessing Near Real-Time Satellite Precipitation Products for Flood Simulations at Sub-Daily Scales in a Sparsely Gauged Watershed in Peruvian Andes

This study investigates the applicability of Satellite Precipitation Products (SPPs) in near real-time for the simulation of sub-daily runoff in the Vilcanota River basin, located in the southeastern Andes of Peru. The data from rain gauge stations are used to evaluate the quality of Integrated Multi-satellite Retrievals for GPM–Early (IMERG-E), Global Satellite Mapping of Precipitation–Near Real-Time (GSMaP-NRT), Climate Prediction Center Morphing Method (CMORPH), and HydroEstimator (HE) at the pixel-station level; and these SPPs are used as meteorological inputs for the hourly hydrological modeling. The GR4H model is calibrated with the hydrometric station of the longest record, and model simulations are also verified at one station upstream and two stations downstream of the calibration point. Comparing the sub-daily precipitation data observed, the results show that the IMERG-E product generally presents higher quality, followed by GSMaP-NRT, CMORPH, and HE. Although the SPPs present positive and negative biases, ranging from mild to moderate, they do represent the diurnal and seasonal variability of the hourly precipitation in the study area. In terms of the average of Kling-Gupta metric (KGE), the GR4H_GSMaP-NRT’ yielded the best representation of hourly discharges (0.686), followed by GR4H_IMERG-E’ (0.623), GR4H_Ensemble-Mean (0.617) and GR4H_CMORPH’ (0.606), and GR4H_HE’ (0.516). Finally, the SPPs showed a high potential for monitoring floods in the Vilcanota basin in near real-time at the operational level. The results obtained in this research are very useful for implementing flood early warning systems in the Vilcanota basin and will allow the monitoring and short-term hydrological forecasting of floods by the Peruvian National Weather and Hydrological Service.

Reverse Flood Routing in an Open Channel Using Genetic Algorithm

Flood routing in flood forecasting issue, calculation the height of flood bands, determining the river boundaries, and estimation of protective facilities for flood –exposed building is applicable. In many cases, due to the lack of measuring stations, the status of the upstream flood generating hydrograph is not known. The purpose of this study is to present an integrated method comprising of an optimization model and a hydrodynamic numerical model for flood modeling to determine the upstream hydrograph using the provided hydrograph at the downstream measuring station of a river. The routing procedure consists of three steps: (1) generating a hypothetical upstream hydrograph using genetic algorithm method; (2) hydrodynamic modeling using a numerical simulation model for flood routing according to the hypothetical hydrograph which is generated in the first step; (3) compare the calculated and observed hydrograph in downstream by using a fitness function. This recommended procedure was named as Reverse Flood Routing Method (RFRM) and was then applied to Karun River, the largest river in Iran. Comparing the generated upstream hydrograph by the RFRM model with the corresponding measured hydrograph at Ahvaz hydrometric station, as an ungauged river location, shows the high accuracy of the recommended model in this study.