Prediction of erosion and sedimentation rates using SWAT (Soil and Water Assessment Tool) method in the Jenelata Sub Watershed

Erosion and sedimentation are problems that often occur in watershed ecosystems. The SWAT model (Soil and Water Assessment Tool) can be used to determine the output of a watershed’s performance. Jenelata sub-watershed area is one of the largest sub-watersheds of the Jeneberang watershed with 22.800 ha. This study aims to determine the spatial distribution of the hydrologic response unit (HRU) and analyze the rate of erosion and sedimentation in the Jenelata sub-watershed. The results showed that most HRUs are in secondary dryland forests with 447 HRU (19.09%). The level of erosion in the very light category, namely 5.74 ton/ha/year (37.53%) and light 34.71 ton/ha/year (27.76%), was in the villages of Moncongloe, Tana Karaeng, Sicini, Paladindang, Towata, Parang Lampoa, Manuju, and Buakkang. Meanwhile, moderate erosion was 104.07 ton/ha/year (23.92%), high 289.65 ton/ha/year (9.59%), and very high 553.74 ton/ha/year (1.20%) located in the villages of Pattallikang, Mangempang, Bontomanai, Bissoloro, Rannaloe, Jenebatu, and Sapaya. The largest sedimentation is 133.18 ton/ha/year in sub-watershed17, located in Bissoloro and Rannaloe villages.

Application of the SWAT hydrological model in flow and solid discharge simulation as a management tool of the Indaia River Basin, Alto São Francisco, Minas Gerais

Measurement and evaluation of soil erosion and consequent sediment yield are fundamental in the planning and management of watersheds, as they allow the identification of critical areas susceptible to erosive processes. This study analyzed the sediment yield generated by water erosion in the Indaia River Basin, Alto São Francisco, Minas Gerais, by using the SWAT hydrological model. From a regional/local scale, the initial simulation of the variables (flow and solid discharge) was performed on a monthly scale from 1988 to 2017. Then, parameter-sensitivity analysis, calibration, and validation of the model were executed. In the monthly calibration (1988 to 2007), the performance of the simulations for flow was R2=0.92 and NSE=0.91 and for total solid discharge R2=0.51 and NSE=0.50. In the monthly validation (2008 to 2017) for flow, R2=0.85 and NSE=0.82 was obtained and for total solid discharge R2=0.19 and NSE=0.16. Despite the unsatisfactory result in the validation stage, the model was able to analyze the distribution of sediment production by sub-basins or even by the Hydrologic Response Unit (HRU). Therefore, a sediment-yield map was generated which qualitatively indicated a tendency for greater erosive processes in the central portion of the basin. The results will support public policies mitigating environmental degradation of the Indaia River Basin.

Analisa Erosivitas Lahan Pada DAS Batang Agam Dengan Menggunakan Model SWAT

Batang Agam River is a river that flows along Bukittinggi City, Agam Regency, Fifty Cities Regency, Tanah Datar Regency, Payakumbuh City and empties into the Batang Sinamar River. The Batang Agam River, which crosses the residential area, is mostly used for agriculture, fisheries, tourism, mining and other activities. Community activities in the area of the Batang Agam River have resulted in a decrease in the function of the river which is characterized by narrowing, silting and pollution of the river. In addition, it is influenced by the ever-changing river water discharge, land conditions and changes that occur in the river channel. In addition, the negative behavior of the community and all their activities around the river basin also triggers damage to the river. This research was conducted to determine the value of land erosivity towards changes in land use area in the Batang Agam watershed, in this study using land cover in 2010, 2014, and 2019 using the Soil and Water Assessment Tool (SWAT) model and manual calculations were carried out using the USLE method as ratio. The analysis consists of four processes, namely watershed delineation, formation of a Hydrologic Response Unit (HRU), formation of climatological data, and finally the simulation process. The results of the HRU analysis showed that the Batang Agam watershed became 7 sub-watersheds, the dominant HRU was primary forest. In the case of Batang Agam, it was found that the level of erosion hazard was in the light category and the erosion value decreased in each review year.

Analisa Debit Andalan Pada Das Batang Arau Dengan Menggunakan Model Soil And Water Assessment Tool (SWAT)

Batang Arau is one of the rivers that flows in the city of Padang, the upstream part of the Batang Arau watershed starting from the Lubuk Paraku river which is in the northeast of Padang City, with a water catchment area of 2,504 hectares which is Dr. Muhammad Hatta, Nature Reserve Area Barisan I and Arau downstream. The Batang Arau watershed has decreased its primary forest area due to the large number of additional settlements. The increase in residential area resulted in the land that was previously not waterproof. The mainstay discharge always increases in the rainy season and decreases in the dry season. The mainstay discharge in the Batang Arau watershed is calculated using the Fj Mock method. The Batang Arau watershed also knows the mainstay discharge that occurred in 2010, 2012 and 2018 using the Soil and Water Assessment Tool (SWAT) model. The analysis was obtained from four processes, namely delineation of the watershed, the formation of the Hydrologic Response Unit (HRU), the formation of climatological data, and the simulation process. For the HRU analysis of the Batang Arau watershed, it was obtained 7 sub-watersheds, the dominant HRU, namely primary dryland forest, was 74.68%.

Analisa Pengaruh Perubahan Penggunaan Lahan terhadap DAS Batang Kuranji dengan Menggunakan Model Soil and Water Assessment Tool (SWAT)

Batang Kuranji is one of six rivers that flow in the city of Padang, and is the main source of water for residents of Padang City to meet the raw water which is then processed into clean water and the needs of Mt. Nago irrigation water. The increase in population causes the population to move to a higher area (green zone). Batang Kuranji watershed has experienced a reduction in the area of forest land due to changes in land use activities by the population movement. As a result, land that was not watertight at first became watertight, the mainstay discharge or expected discharge is always available, always increasing during the rainy season and decreasing during the dry season. This study was conducted to determine the extent of land use change in the Batang Kuranji watershed, also to determine the main discharge that occurred in 2009, 2011, and 2017 using the Soil and Water Assessment Tool (SWAT) model. The analysis consists of four processes, namely watershed delineation, formation of a Hydrologic Response Unit (HRU), formation of climatological data, and finally the simulation process. HRU analysis results obtained by Batang Kuranji watershed into 9 sub-watersheds, the dominant HRU is protection forest by 62%, soil type with depth (solum) level A and B, runoff coefficient of 0.3 and NS value obtained by 0.6. This shows that the SWAT model can predict the hydrological process in the upstream Batang Kuranji watershed. The most influential land use on surface runoff is land use for settlement.

Application of deep recurrent neural networks for modeling surface and sub-surface flow at high temporal resolution

<p>Recent increase in climate change has resulted in rise of hydrologic extreme events, which demands better understanding of flow patterns in catchment. Modeling surface and sub-surface flow at high temporal resolution helps to understand catchment dynamics. In this study, we simulated surface and sub-surface flow in a Laotian catchment at 6-minute resolution. We used one physically based model called Hydrological Simulated Program-FORTRAN (HSPF) and developed two deep learning-based models. One deep learning model consisted of only one long short-term memory (LSTM), whereas the other model simulated processes in each hydrologic response unit (HRU) by defining one separate LSTM for each HRU. The models consider environmental data as well as changing landuse in catchment and predict surface and sub-surface flows. Our results show that simple LSTM model outperformed other models for surface runoff prediction, whereas the HRU-based LSTM model better predicted patterns and slopes in sub-surface flow in comparison with other models.</p>