Suitability of Watershed Models to Predict Distributed Hydrologic Response in the Awramba Watershed in Lake Tana Basin

Moges, Mamaru A.; Schmitter, Petra; Tilahun, Seifu A.; Langan, Simon; Dagnew, Dessalegn C.; Akale, Adugnaw T.; Steenhuis, Tammo S.

doi:10.1002/ldr.2608

Cited by 34 publications

(30 citation statements)

References 83 publications

(122 reference statements)

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“…Previous hydrological studies have also shown wide applicability of SWAT for hydrometeorologically similar catchments and in other parts of Ethiopian basins [42][43][44][45][46][47][48].…”

Section: Model Descriptionmentioning

confidence: 99%

Streamflow and Sediment Yield Prediction for Watershed Prioritization in the Upper Blue Nile River Basin, Ethiopia

Ayele

Teshale

et al. 2017

Water

115

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Inappropriate use of land and poor ecosystem management have accelerated land degradation and reduced the storage capacity of reservoirs. To mitigate the effect of the increased sediment yield, it is important to identify erosion-prone areas in a 287 km 2 catchment in Ethiopia. The objectives of this study were to: (1) assess the spatial variability of sediment yield; (2) quantify the amount of sediment delivered into the reservoir; and (3) prioritize sub-catchments for watershed management using the Soil and Water Assessment Tool (SWAT). The SWAT model was calibrated and validated using SUFI-2, GLUE, ParaSol, and PSO SWAT-CUP optimization algorithms. For most of the SWAT-CUP simulations, the observed and simulated river discharge were not significantly different at the 95% level of confidence (95PPU), and sources of uncertainties were captured by bracketing more than 70% of the observed data. This catchment prioritization study indicated that more than 85% of the sediment was sourced from lowland areas (slope range: 0-8%) and the variation in sediment yield was more sensitive to the land use and soil type prevailing in the area regardless of the terrain slope. Contrary to the perception of the upland as an important source of sediment, the lowland in fact was the most important source of sediment and should be the focus area for improved land management practice to reduce sediment delivery into storage reservoirs. The research also showed that lowland erosion-prone areas are typified by extensive agriculture, which causes significant modification of the landscape. Tillage practice changes the infiltration and runoff characteristics of the land surface and interaction of shallow groundwater table and saturation excess runoff, which in turn affects the delivery of water and sediment to the reservoir and catchment evapotranspiration.

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“…Previous hydrological studies have also shown wide applicability of SWAT for hydrometeorologically similar catchments and in other parts of Ethiopian basins [42][43][44][45][46][47][48].…”

Section: Model Descriptionmentioning

confidence: 99%

Streamflow and Sediment Yield Prediction for Watershed Prioritization in the Upper Blue Nile River Basin, Ethiopia

Ayele

Teshale

et al. 2017

Water

115

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“…In some of our fields, infiltration rates increased to 242 mm hr −1 under deep tillage, further reducing the expected runoff and soil loss. As the subhumid mountainous region of Amhara is characterized by saturation excess flow [26,27], breaking the hardpan in the upland area leads to increase subsurface water availability and, therefore, shallow groundwater recharge by decreasing surface runoff.…”

Section: Effect Of Deep Tillage On Soil Erosion and Runoffmentioning

confidence: 99%

Deep Tillage Improves Degraded Soils in the (Sub) Humid Ethiopian Highlands

Hussein

Muche

Schmitter

et al. 2019

Land

Self Cite

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Intensification of rainfed agriculture in the Ethiopian highlands has resulted in soil degradation and hardpan formation, which has reduced rooting depth, decreased deep percolation, and increased direct runoff and sediment transport. The main objective of this study was to assess the potential impact of subsoiling on surface runoff, sediment loss, soil water content, infiltration rate, and maize yield. Three tillage treatments were replicated at five locations: (i) no tillage (zero tillage), (ii) conventional tillage (ox-driven Maresha plow, up to a depth of 15 cm), and (iii) manual deep ripping of the soil's restrictive layers down to a depth of 60 cm (deep till). Results show that the posttreatment bulk density and penetration resistance of deep tillage was significantly less than in the traditional tillage and zero-tillage systems. In addition, the posttreatment infiltration rate for deep tillage was significantly greater, which resulted in significantly smaller runoff and sedimentation rates compared to conventional tillage and zero tillage. Maize yields were improved by 6% under deep tillage compared to conventional tillage and by 29% compared to no tillage. Overall, our findings show that deep tillage can be effective in overcoming some of the detrimental effects of hardpans in degraded soils.

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“…However, the difference is that a human only processes certain information at certain times, but Deep Learning, which are developed with the same concept as human neurons can process thousands of pieces of information in a much shorter time [6]. Different principles have been used to forecast floods, such as computer simulations based on the watershed demographic model, principle of hydrological, hydraulic components and groundwater flow model [7]. However, these methods only can predict certain catchment or basin based on certain water-level value.…”

Section: Introductionmentioning

confidence: 99%

Flood prediction of Sungai Bedup, Serian, Sarawak, Malaysia using deep learning

Yahya¹,

Tei²

2018

IJET

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This paper aims to evaluate the performance of the Long Short Term Memory (LSTM) model for flood forecasting. Seven data sets provided by the Drainage and Irrigation Department (DID) for Sungai Bedup, Serian, Sarawak, Malaysia are used for evaluating the performance of LSTM algorithm. Distinctive network was trained and tested using daily data obtained from the DID with the year range from 2014 to 2017. The performance of the algorithm was evaluated based on (Training Error Rate, Testing Error Rate, Loss, Accuracy, Validate Loss and Validate Accuracy) and compared with the Backpropagation Network (BP). Among the seven data sets, Sungai Bedup showed small testing error rate which is (0.08), followed by Bukit Matuh (0.11), Sungai Teb (0.14), Sungai Merang (0.15), Sungai Meringgu (0.12), Semuja Nonok (0.14) and lastly Sungai Busit is (0.13). Moreover, the developed model performance is evaluated by comparing with BP model. Results from this research evidently proved LSTM models is reliable to forecasting flood with the lowest testing error rate which is (0.08) and highest validate accuracy (92.61% ) compared to BP with testing error rate (0.711) and validate accuracy (85.00%). Discussion is provided to prove the effectiveness of the model in forecasting flood problems.

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Suitability of Watershed Models to Predict Distributed Hydrologic Response in the Awramba Watershed in Lake Tana Basin

Cited by 34 publications

References 83 publications

Streamflow and Sediment Yield Prediction for Watershed Prioritization in the Upper Blue Nile River Basin, Ethiopia

Streamflow and Sediment Yield Prediction for Watershed Prioritization in the Upper Blue Nile River Basin, Ethiopia

Deep Tillage Improves Degraded Soils in the (Sub) Humid Ethiopian Highlands

Flood prediction of Sungai Bedup, Serian, Sarawak, Malaysia using deep learning

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