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.

Gajahwong River Continuous Flow Simulation by Using Soil Moisture Accounting (SMA) of HEC HMS

Gajahwong River is located in the southern part of Java Island, Indonesia, specifically on Daerah Istimewa Yogyakarta. Gajahwong River has an important role along the area where its discharge is used especially for irrigation water supply. Other factors stressing Gajahwong basin’s water supply purpose are population number rise and land cover change that influence river streamflow in the whole year. Hence, a continuous flow simulation was conducted to analyze Gajahwong River water availability to supply its requirement. The Soil Moisture Accounting (SMA) module of HEC-HMS was employed to simulate the continuous flow of Gajahwong River, particularly in Wonokromo Outlet. The continuous flow was simulated from the year 2012 until 2015 based on observed discharge data availability. Comparison between simulated and observed discharge was quantified by using R2, Nash-Sutcliffe efficiency and PBIAS statistic value. The statistic above parameter values yielded a value of 0.90, 0.58, and 2.02%, respectively for daily time series simulation.

Naturalized streamflows and Affluent Natural Energy projections for the Brazilian hydropower sector for the SSP2-4.5 and SSP5-8.5 scenarios of the CMIP6

Climate change projects an increase in extreme weather events in the coming decades, which could significantly affect Brazil's water and energy security. Thus, this study sought to analyze possible impacts of climate change on the projections of naturalized streamflows and Affluent Natural Energy (ANE) for the Brazilian hydropower sector utilizing five models of the Coupled Model Intercomparison Projects version 6 (CMIP6), based on SSP-4.5 and SSP-8.5 scenarios for the 21st century. Naturalized streamflows for the 24 stations representing the National Interconnected System (NIS) were estimated through the concentrated hydrological model SMAP (Soil Moisture Accounting Procedure), while the streamflows for the other stations that comprise the NIS were obtained by linear regression. The streamflows, as well as the productivity of the reservoirs, were used to calculate the ANE. The results showed that most of the models project possible reductions in annual naturalized streamflows and ANE for the three periods analyzed and for the North, Northeast, and Southeast/Midwest sectors of Brazil. Meanwhile, the Northern and Southern sectors, for the period 2080–2099, most of the models indicated an increase of annual, precipitation, naturalized streamflows and ANE.

Continuous modelling of the Bouregreg watershed (Morocco) using the HEC-HMS model

A deep understanding of the rainfall-runoff mechanism is essential to estimate the runoff generated in a given basin. In this regard, this paper aims to develop a continuous hydrological model of the Bouregreg watershed. The objective of this modelling is to evaluate the inflow to the Sidi Mohamed Ben Abdellah (SMBA) dam, located at the outlet of this basin. To this end, using the HEC-HMS model, the Soil Moisture Accounting (SMA) Loss Method was used to model infiltration losses. The SCS Unit hydrograph (SCS UH) and the Recession method were chosen as transform model and baseflow model, respectively. As a result, the comparison shows an acceptable agreement between observed and simulated flow in terms of streamflow distribution and peak values (NSE=0.57, R2=0.58). During validation, the model retained its ability to sufficiently reproduce the rainfall-runoff mechanism of the studied basin with a slight overestimation of peaks (NSE=0.61, R2=0.60). This study allows to assess and predict the inter-annual and intra-annual variation of the SMBA dam reservoir’ inflows, and therefore to forecast the climate change impact on this basin.

Mean Field Bias-Aware State Updating via Variational Assimilation of Streamflow into Distributed Hydrologic Models

When there exist catchment-wide biases in the distributed hydrologic model states, state updating based on streamflow assimilation at the catchment outlet tends to over- and under-adjust model states close to and away from the outlet, respectively. This is due to the greater sensitivity of the simulated outlet flow to the model states that are located more closely to the outlet in the hydraulic sense, and the subsequent overcompensation of the states in the more influential grid boxes to make up for the larger scale bias. In this work, we describe Mean Field Bias (MFB)-aware variational (VAR) assimilation, or MVAR, to address the above. MVAR performs bi-scale state updating of the distributed hydrologic model using streamflow observations in which MFB in the model states are first corrected at the catchment scale before the resulting states are adjusted at the grid box scale. We comparatively evaluate MVAR with conventional VAR based on streamflow assimilation into the distributed Sacramento Soil Moisture Accounting model for a headwater catchment. Compared to VAR, MVAR adjusts model states at remote cells by larger margins and reduces the Mean Squared Error of streamflow analysis by 2–8% at the outlet Tiff City, and by 1–10% at the interior location Lanagan.

Evaluation of an Application of Probabilistic Quantitative Precipitation Forecasts for Flood Forecasting

Probabilistic streamflow forecasts using precipitation derived from ensemble-based Probabilistic Quantitative Precipitation Forecasts (PQPFs) are examined. The PQPFs provide rainfall amounts associated with probabilities of exceedance for all grid points, which are averaged to the watershed scale for input to the operational Sacramento Soil Moisture Accounting hydrologic model to generate probabilistic streamflow predictions. The technique was tested using both the High-Resolution Rapid Refresh Ensemble (HRRRE) and the High-Resolution Ensemble Forecast version 2.0 (HREF) for 11 river basins across the upper Midwest for 109 cases. The resulting discharges associated with low probability of exceedance values were too large; no events were observed having discharges above the 10% exceedance value predicted from the technique applied to both ensembles, and no events were observed having discharges above the 25% exceedance value from the HREF-based forecast. The large differences are due to using the same precipitation exceedance value at all points; it is unlikely that all watershed points would experience the heavy rainfall associated with the 5% probability of exceedance. The technique likely can be improved through calibration of the basin-average precipitation forecasts based on typical distributions of precipitation within the convective systems that dominate warm-season precipitation events or calibration of the resulting probabilistic discharge forecasts.

Flow Prediction Using Remotely Sensed Soil Moisture in Irish Catchments

Owing to a scarcity of in situ streamflow data in ungauged or poorly gauged basins, remote sensing data is an ideal alternative. It offers a valuable perspective into the dynamic patterns that can be difficult to examine in detail with point measurements. For hydrology, soil moisture is one of the pivotal variables which dominates the partitioning of the water and energy budgets. In this study, nine Irish catchments were used to demonstrate the feasibility of using remotely sensed soil moisture for discharge prediction in ungagged basins. Using the conceptual hydrological model “Soil Moisture Accounting and Routing for Transport” (SMART), behavioural parameter sets (BPS) were selected using two different objective functions: the Nash Sutcliffe Efficiency (NSE) and Coefficient of Determination (R2) for the calibration period. Good NSE scores were obtained from hydrographs produced using the satellite soil moisture BPS. While the mean performance shows the feasibility of using remotely sensed soil moisture, some outliers result in negative NSE scores. This highlights that care needs to be taken with parameterization of hydrological models using remotely sensed soil moisture for ungauged basin.

MODELOS HIDROLÓGICOS SAC-SMA E IPH II: CALIBRAÇÃO E AVALIAÇÃO DO DESEMPENHO NA ESTIMATIVA DE VAZÕES NA BACIA DO RIO PIRACICABA (MG)

MODELOS HIDROLÓGICOS SAC-SMA E IPH II: CALIBRAÇÃO E AVALIAÇÃO DO DESEMPENHO NA ESTIMATIVA DE VAZÕES NA BACIA DO RIO PIRACICABA (MG)4 EDUARDO MORGAN ULIANA1; DEMETRIUS DAVID DA SILVA2; MICHEL CASTRO MOREIRA2; SILVIO BUENO PEREIRA2 E DONIZETE DOS REIS PEREIRA3 1Instituto de Ciências Agrárias e Ambientais, Universidade Federal de Mato Grosso, Avenida Alexandre Ferronato, 1200, Setor Industrial, CEP. 78557-267, Sinop, Mato Grosso, Brasil, [email protected] 2Departamento de Engenharia Agrícola, Universidade Federal de Viçosa, Campus Universitário, CEP. 36570-900, Viçosa, Minas Gerais, Brasil, [email protected], [email protected] 3Instituto de Ciências Agrárias, Universidade Federal de Viçosa, Rodovia LMG 818, km 06, CEP. 35690-000, Florestal, Minas Gerais, Brasil, [email protected] 4 O artigo é referente ao capítulo 1 da tese de doutorado do primeiro autor. 1 RESUMO O objetivo do trabalho foi avaliar o desempenho dos modelos conceituais chuva-vazão Soil Moisture Accounting (SAC-SMA) e IPH II para a estimativa das vazões diárias na bacia hidrográfica do rio Piracicaba (MG), de modo que os mesmos possam ser utilizados para subsidiar o planejamento e a gestão de recursos hídricos na bacia. O estudo foi realizado em três seções de monitoramento de vazão da bacia do rio Piracicaba, localizada no estado de Minas Gerais - Brasil. A calibração dos modelos foi realizada com o algoritmo SCE-UA, utilizando como função objetivo o índice de Nash-Sutcliffe. Os valores do índice de eficiência de Nash-Sutcliffe obtidos nas três seções de monitoramento foram de 0,87, 0,78 e 0,71 para o modelo SAC-SMA e de 0,88, 0,80, e 0,73 para o modelo IPH II, confirmando que os modelos são “adequados e bons” para a estimativa das vazões diárias. Concluiu-se, portanto, que os modelos SAC-SMA e IPH II são adequados para a estimativa das vazões diárias de cursos de água da bacia do rio Piracicaba (MG), demonstrando potencial para serem utilizados em estudos relacionados com simulação hidrológica e gestão de recursos hídricos em bacias hidrográficas de regiões tropicais. Palavras-chave: modelo concentrado, chuva-vazão, planejamento de recursos hídricos, previsão. ULIANA, E. M.; SILVA, D. D.; MOREIRA, M. C.; PEREIRA, S. B.; PEREIRA, D. R. SAC-SMA AND IPH II HYDROLOGICAL MODELS: CALIBRATION AND PERFORMANCE ASSESSMENT FOR WATER FLOW ESTIMATE IN PIRACICABA RIVER BASIN (MG) 2 ABSTRACT The aim of this study was to evaluate the performance of conceptual models Sacramento - Soil Moisture Accounting (SAC-SMA) and IPH II for daily flow estimation in Piracicaba river basin, Minas Gerais, so that they may be used in the planning and management of water resources in the river basin. The study was carried out in three runoff-monitoring sections on Piracicaba river basin, located in the State of Minas Gerais - Brazil. Model calibration was performed through the Shuffled Complex Evolution (SCE-UA) algorithm, whose objective function was the Nash-Sutcliffe efficiency index. The Nash-Sutcliffe efficiency index values obtained in the three monitoring-sections were 0.87, 0.78, and 0.71 for the SAC-SMA model, and 0.88, 0.80, and 0.73 for the IPH II one. These values confirm that the models are “appropriate and good” for daily flow estimations. Overall, both models can be further used for hydrologic simulations and water resources management in tropical river basins. Keywords: concentrated models, rainfall-runoff, planning of water resources, forecast.

Analisis Hidrologi Model Soil Moisture Accounting Menggunakan Program HEC-HMS (Studi Kasus : DAS Rokan AWLR Pasir Pangaraian)

(ID) Hidrologi suatu Daerah Aliran Sungai (DAS) rumit untuk diprediksi karena kekurangan data dan membutuhkan biaya yang mahal. Pada penelitian ini mengambil lokasi di sub DAS Rokan Hulu stasiun Pasir Pengaraian yang hampir setiap tahun terjadi banjir. Perencanaan dan pengolahan sumber daya air di suatu wilayah daerah aliran sungai sangat penting, maka dari itu perlunya mengetahui karakteristik suatu DAS. Perencanaan dan pengolaan sumber daya air memerlukan data debit aliran yang lengkap. Pada sub DAS ini data hujan, data debit dan data klimatologi menggunakan periode data sepuluh tahun yaitu dari tahun 2008-2017. Pemodelan hidrologi dilakukan pendekatan dengan beberapa metode, salah satunya metode soil moisture accounting di program HEC-HMS yang mana metode tersebut mensimulasikan suatu pergerakan air pada vegetasi, permukaan tanah dan di bawah permukaan tanah. Penyusunan periode kalibrasi dan verifikasi disusun dalam sembilan skema yang diharapkan mampu menghasilkan hasil yang paling optimal. Sembilan skema untuk Kalibrasi dan Verifikasi ini menggunakan metode objective function yaitu percent error in discharge volume. Skema yang paling optimal adalah skema VII (7 tahun kalibrasi 3 tahun Verifikasi), dengan nilai verifikasi 10,1%”Baik” dan Kalibrasi 0,0% “Sangat Baik”. (EN) The hydrology of a watershed is difficult to predict because of the lack of data and requires high costs. In this study taking location in the Rokan Hulu sub-watershed, Pasir Pengaraian station Almost every year flooding occurs. Planning and management of water resources in a watershed is very important and therefore it is necessary to know the characteristics of the watershed. Planning and management of water resources require complete data. in this sub-watershed rainfall data, discharge data and climatology data use a ten-year data period from 2008-2017. Hydrological modeling is approached with several methods, one of them is soil moisture accounting method in the HEC-HMS program where the method simulates a movement of water on vegetation, soil surface and below ground level. The preparation of the calibration and verification periods arranged in nine schemes is expected to produce the most optimal results. The nine schemes for Calibration and Verification use the objective function method, which is the percentage error in discharge volume. The most optimal scheme is the scheme VII (7 years calibration 3 years Verification), with a verification value of 10.1% "Good" and Calibration 0.0% "Very Good".