Kalibrasi Model Soil Moisture Accounting dengan Software HEC-HMS di Stasiun Debit Pasir Pangaraian

This research aims to compare the results of the calibration of the Soil Moisture Accounting (SMA) model using Percent Error in Volume (PEV) and Peak Weighted Root Mean Square Error (RMSE). The SMA model calibration uses the HEC-HMS (Hydrologic Engineering Center – Hydrologic Modeling System). There are 12 calibrated parameters by automatic calibration. The input data are the area of ​​the watershed, daily rainfall, daily discharge data and climatological data. The data used is data from 2008 to 2017. The results show that PEV performance shows good results. While the RMSE showed poor results. PEV results are best at 7 years of calibration and 3 years of verification. The length of the calibration data has not affected the verification results.

Climate Change Effects on Fish Passability across a Rock Weir in a Mediterranean River

Climate change represents a major challenge for the management of native fish communities in Mediterranean rivers, as reductions in discharge may lead to a decrease in passability through small barriers such as weirs, both in temporary and perennial rivers. Through hydraulic modelling, we investigated how discharges from a large hydropower plant in the Tagus River are expected to affect the passability of native freshwater fish species through a rock weir (Pego, Portugal), equipped with a nature-like fish ramp. We considered not only mean daily discharge values retrieved from nearby gauging stations (1991–2005) for our flow datasets, but also predicted discharge values based on climatic projections (RCP) until the end of the century (2071–2100) for the Tagus River. Results showed that a minimum flow of 3 m3 s−1 may be required to ensure the passability of all species through the ramp and that passability was significantly lower in the RCP scenarios than in the historical scenario. This study suggests that climate change may reduce the passability of native fish species in weirs, meaning that the construction of small barriers in rivers should consider the decreases in discharge predicted from global change scenarios for the suitable management of fish populations.

Pollution Characteristics and Emissions of Typical Organophosphate Esters From a Wastewater Treatment Plant in Chengdu, China

Effluent from wastewater treatment plants (WWTP) is an important source of organophosphate esters (OPEs) in the receiving rivers. In this paper, the concentration and distribution of seven OPEs in the water samples were determined, and the discharge was estimated. The results showed that the total removal rate of Σ7OPEs in water phase in WWTP was 57.2%. The average concentrations of Σ7OPEs in influent and effluent of the WWTP during rainy period were 3956.1 ± 1897.3 ng/L and 1461.9 ± 846.3 ng/L, respectively, which were about 4 times larger than those in influent water (978.2 ± 166.5 ng/L) and effluent (418.3 ± 12.0 ng/L) during non-rainy period, indicating that rainfall has a marked impact on the load of OPEs in WWTP and the receiving water. It was estimated that the average daily discharge of Σ7OPEs in the effluent of WWTP was 157.9 g, and the daily per capita contribution of the population in the area to the OPEs in the influent was 0.414 mg. During the shift of labour-intensive manufacturing from the coastal developed areas to inland regions, OPEs were widely used and produced in Southwest China. The total amount of OPEs emissions and its control should be taken into consideration.

High temporal resolution hydrometeorological data collected in the tropical Cordillera Blanca, Peru (2004–2020)

This article provides a comprehensive hydrometeorological dataset collected over the past two decades throughout the Cordillera Blanca, Peru. The data recording sites, located in the upper portion of the Rio Santa valley, also known as the Callejon de Huaylas, span an elevation range of 3738–4750 m a.s.l. As many historical hydrological stations measuring daily discharge across the region became defunct after their installation in the 1950s, there was a need for new stations to be installed and an opportunity to increase the temporal resolution of the streamflow observations. Through inter-institutional collaboration the hydrometeorological network described in this paper was deployed with goals to evaluate how progressive glacier mass loss was impacting stream hydrology, and to better understand the local manifestation of climate change over diurnal to seasonal and interannual time scales. The four automatic weather stations supply detailed meteorological observations, and are situated in a variety of mountain landscapes, with one on a high-mountain pass, another next to a glacial lake, and two in glacially carved valleys. Four additional temperature and relative humidity loggers complement the weather stations within the Llanganuco valley by providing these data across an elevation gradient. The six streamflow gauges are located in tributaries to the Rio Santa and collect high temporal resolution runoff data. The datasets presented here are available freely from https://doi.org/10.4211/hs.059794371790407abd749576df8fd121 (Mateo et al., 2021). Combined, the hydrological and meteorological data collected throughout the Cordillera Blanca enable detailed research of atmospheric and hydrological processes in tropical high-mountain terrain.

Prediction of Daily Streamflow Using Various Kernel Function Based Regression: A Case Study in India

River daily discharge estimation and modeling considers an important step for scheduling and planning different water resources for sustainable socio-economic development. In the current work, four techniques of Gaussian processes regression (GPR): Polynomial Kernel, Radial Basis Function Kernel, Normalized Polynomial Kernel, and PUK Kernel, were used to model the daily discharge. Hydrological-datasets containing daily-stage (m) and discharge (m3/sec) were gathered over the period from 2004-2013. The datasets were divided into two sections: (i) models training containing 70% (2004-2010) of the total data and (ii) remaining 30% (2011- 2013) were for testing. Comparing all the four developed models, our findings show that the superlative model was the PUK-Kernel model with a correlation coefficient (r) of 0.96, MAE of 36.70 m3/s, RMSE of 90.92 m3/s, RAE of 17.50 %, RRSE of 26.05 % in the training period. Whereas, it performed equally well in the testing period with r = 0.97, MAE = 44.84 m3/s, RMSE = 95.05 m3/s, RAE = 17.98 %, RRSE = 24.94 % in the testing period. Our findings can be included that GPR-PUK was more accurate and stable than other models, and can be used to help water-users, decision-makers, development-planners for managing water resources and achieving sustainable development.

Machine-learning and HEC-RAS integrated models for flood inundation mapping in Baro River Basin, Ethiopia

This study presents an integrated machine-learning and HEC-RAS models for flood inundation mapping in Baro River Basin, Ethiopia. ANN and HEC-RAS models were integrated as a predictive hydrological and hydraulic model to generate runoff and the extent of flood, respectively. Daily rainfall and temperature data of 7-years (1999–2005), daily discharge (1999–2005) and 30 m × 30 m gridded Topographical Wetness Index (TWI) were used to train a predictive ANN hydrological model in RStudio. The predictive performance of the developed ANN hydrological model was evaluated in RStudio using Nash–Sutcliffe Efficiency (NSE) values of 0.86 and 0.88 during the training period (1999–2005) and testing period (2006–2008), respectively, with the corresponding observed daily discharge. The validated ANN predictive hydrological model was linked with HEC-RAS to generate the flood extent along the river course. The HEC-RAS model result was calibrated and validated using the water body delineated using Normal Difference Water Index (NDWI) from LANDSAT 8 imagery based on historical flood events of 2005 and 2008. It was found that about 96% of an agreement was made between the flood-prone areas generated in HEC-RAS and the water body delineated using NDWI. Therefore, it is logical to conclude that the integration of a machine-learning approach with the HEC-RAS model has improved the spatiotemporal uncertainties in traditional flood forecasting methods. This integrated model is powerful tool for flood inundation mapping to warn residents of this basin.

Insights from an Evaluation of Nitrate Load Estimation Methods in the Midwestern United States

This study investigated the accuracy and suitability of several methods commonly used to estimate riverine nitrate loads at eight watersheds located southwest of Lake Erie in the Midwestern United States. This study applied various regression methods, including a regression estimator with five, six, and seven parameters, an estimator enhanced by composite, triangular, and rectangular error corrections with residual and proportional adjustment methods, the weighted regressions on time, discharge, and season (WRTDS) method, and a simple linear interpolation (SLI) method. Daily discharge and nitrate concentration data were collected by the National Center for Water Quality Research. The methods were compared with subsampling frequencies of 6, 12, and 24 times per year for daily concentrations, daily loads, and annual loads. The results indicate that combinations of the seven-parameter regression method with composite residual and rectangular residual adjustments provided the best estimates under most of the watershed and sampling frequency conditions. On average, WRTDS was more accurate than the regression models alone, but less accurate than those models enhanced by residual adjustments, except for the most urbanized watershed, Cuyahoga. SLI was the most accurate in the Vermilion and Maumee watersheds. The results also provide some information about the effects of rating curve shape and slope, land use, and record length on model performance.

http://method.meteorf.ru/publ/tr/tr380/htm/02.htm

Forecasting the flow of Russian rivers by hydrograph extrapolation / Borsch S.V., Koliy V.M., Semenova N.K., Simonov Yu.A., Khristoforov A.V. // Hydrometeorological Research and Forecasting, 2021, no. 2 (380), pp. 77-94. An automated system has been developed based on the hydrograph extrapolation method, which allows the year-round daily forecasting of water level and streamflow for the Russian rivers with up to 10-day lead time. The forecast of discharges or water levels is expressed by a linear formula depending on their values on the date of the forecast issue and five previous days. The forecasting scheme limits the possible minimum and maximum values of the discharge or water level based on historical data. Forecast schemes were obtained for 2776 river gauges. The time period from 2010 to 2019 with daily observations of discharge and water level was used. The forecast verification shows that this method can be successfully applied to large rivers with smooth hydrographs. Keywords: daily discharge and water levels, short- and medium-term forecasts, hydrograph extrapolation method, forecast verification, maximum lead time of satisfactory forecasts, self-learning of an automated system for preparing and issuing forecasts

HYDROLOGICAL ASPECT OF BATANG ALAI WEIR WATERSHED DUE TO LAND USE CHANGES INTO MINING AREAS

The policy of the Ministry of Energy and Mineral Resources that provides permits for mining production activities for PT. MCM in the Batang Alai River area has an impact on the hydrology of the area. This study will discuss the changes in land use in the Batang Alai River area, Batang Alai Timur District, Hulu Sungai Tengah to the aspects of high flow and low flow hydrology. The research method used is manual calculation with rational methods for high flow hydrology and calculations using the ArcSWAT 2012.10.1.18 for low flow hydrology. The data needed in the processing are rainfall data, DEM data, daily discharge data, climatology data, land use data, land use change data, and Batang Alai watershed data. The results of the study were that the ratio of peak discharge of land use was changed to the mining area and land use was not converted into a mining area for high flow hydrology calculations. There is an increase in plan flood discharge in the Batang Alai River if there is a change in land use into a mining area, the design flood discharge always increases in each period. Whereas for low flow hydrology is the comparison between the daily use of land use converted into a mining area and land use is not converted into a mining area, then the daily discharge results are calculated for the availability of water. The results of low-flow hydrological modeling are categorized as good and have high influence based on the Nash-Sutcliffe efficiency value grouping and the coefficient of determination, and water availability decreases if land use is converted into a mining area.

Stored water in the inner bark and sapwood: Atypical patterns of daily discharge and recharge, radial osmotic gradient and freezing resistance

Stored water in inner tissues can affect plant water balance and its freezing resistance. We studied the water storages in the inner bark and sapwood of Araucaria araucana, a species with thick inner bark. Specifically, we analyzed its daily behavior, the driving force to radial water movement and its freezing resistance. The whole-stem water content and diameter and sap flow increased in the morning and decreased in the afternoon. An osmotic gradient between stem tissues was involved in the morning water storage recharge. There were no lags in the onset of sap flow between different stem positions, however sap flow at 6m height was higher than basal sap flow in the afternoon, at the time that sapwood water content started to decline followed by the inner bark. Extracellular freezing was delayed down to -6˚C in the inner bark and to -8˚C in the leaves. The unusual diurnal pattern of internal water use may enhance freezing resistance as a consequence of the lower water content and higher osmotic potential when the lowest temperatures occur. The contribution of stem tissues to daily water use and the pattern of ice nucleation observed make this species less susceptible to drought and very low temperatures.