KARAKTERISTIK HIDROLOGI DAN DAMPAKNYA TERHADAP BANJIR DAERAH ALIRAN SUNGAI JAMBO AYE DI ACEH INDONESIA (Hydrological Characteristics And Its Impact On Flood Jambo Aye Basin In Aceh Indonesia)

Daerah Aliran Sungai (DAS) mengalami peningkatan kerusakan akibat hubungan dan interferensi manusia yang berdampak pada kesetimbangan hidrologi sehingga terjadi bencana. Bencana banjir yang merupakan bencana hidrologi rutin terjadi di DAS Jambo Aye. Penelitian ini bertujuan untuk menganalisa hubungan karakteristik hidrologi DAS Jambo Aye terhadap potensi dan kejadian banjir di wilayah DAS Jambo Aye. Karakeristik kemiringan lereng, pergerakan tanah, analisis kelompok hidrologi tanah (HSG), Antecedent Precipitation Index (API) dan kurva limpasan digunakan sebagai indikator hidrologi untuk mengidentifikasi potensi banjir yang terjadi di DAS Jambo Aye. Analisis menunjukkan DAS Jambo Aye termasuk wilayah yang berpotensi mengalami banjir. Potensi banjir genangan terdapat di wilayah hilir yang mempunyai karakteristik lereng landai (12%), kapasitas infiltrasi sangat lambat (37,95%), nilai kurva limpasan tinggi (17%) dan tingkat kebasahan yang tinggi (26%). Banjir bandang berpotensi terjadi di wilayah hulu yang mempunyai karakteristik lereng curam, adanya pergerakan tanah dan curah hujan yang tinggi. Pengelolaan DAS perlu dilakukan di DAS Jambo Aye mengingat kejadian banjir yang sering terjadi.

Application of a Novel Artificial Neural Network Model in Flood Forecasting

In this paper, a novel ANN flood forecasting model is proposed. The ANN model is combined with traditional hydrological concepts and methods, takes the initial Antecedent Precipitation Index (API), rainfall, upstream inflow and initial flow at the forecast river section as input of model. The distributed rainfall is realized as the input of the model. The simulation is processed by dividing the watershed into several rainfall-runoff processing units. Two hidden layers are used in the ANN, and the topology of ANN is optimized by connecting the hidden layer neurons only with the input which has physical conceptual causes. Topological structure of the proposed ANN model and its information transmission process are more consistent with the physical conception of rainfall-runoff, and the weight parameters of the model are reduced. The arithmetic moving-average algorithm is added to the output of the model to simulate the pondage action of the watershed. Satisfactory results have been achieved in the upstream of Second Songhua river in Songhua basin from the Baishan reservoir to Fengman reservoir sections, and the Mozitan and Xianghongdian reservoirs in the upper reaches of Pi river in Huaihe Basin in China.

How Do Ground Litter and Canopy Regulate Surface Runoff?—A Paired-Plot Investigation after 80 Years of Broadleaf Forest Regeneration

Relatively minimal attention has been given to the hydrology of natural broadleaf forests compared to conifer plantations in Japan. We investigated the impacts of ground litter removal and forest clearing on surface runoff using the paired runoff plot approach. Plot A (7.4 m2) was maintained as a control while plot B (8.1 m2) was manipulated. Surface runoff was measured by a tipping-bucket recorder, and rainfall by a tipping-bucket rain gauge. From May 2016 to July 2019, 20, 54, and 42 runoff events were recorded in the no-treatment (NT), litter removed before clearcutting (LRBC), and after clearcutting (AC) phases, respectively. Surface runoff increased 4× when moving from the NT to LRBC phase, and 4.4× when moving from the LRBC to AC phase. Antecedent precipitation index (API11) had a significant influence on surface runoff in the LRBC phase but not in the NT and AC phases. Surface runoff in the AC phase was high regardless of API11. The rainfall required for initiating surface runoff is 38% and 56% less when moving from the NT to LRBC, and LRBC to AC phases, respectively. Ground litter and canopy function to reduce surface runoff in regenerated broadleaf forests.

A study on an extensively applicable method for determining snowmelt-induced landslides warning periods based on a hydrological index

<p>In snow-covered regions, a large number of landslides are induced by infiltration of snowmelt water. Although it is very important to early find signs of increase in landslide activity such as cracks or bulges for preventing or mitigating snowmelt-induced landslide disasters, thick snow cover often makes it difficult to find them. In such cases, frequent patrols of slopes prone to landslides during periods with high risk can be effective. In Japan, snowmelt advisories are issued by the Japan Meteorological Agency while snowmelt-induced disasters (e.g., flood and landslides) are predicted based on meteorological conditions. Although it seems that snowmelt advisories can be used for judging whether patrols are required, it has been reported that snowmelt advisories are not issued for some days with high risk of snowmelt-induced landslides (Irasawa et al, 2011). Focused exclusively on landslides, Nakaya et al (2008) and Touhei et al (2016) proposed methods for capturing 70% of landslides by setting a critical level using reservoir inflow and river water level and flow rate as hydrological indices. These methods, however, are difficult to apply for areas affected by human impacts including irrigation and water intake and drainage of power stations. In this study, based on the antecedent precipitation index, reported as a hydrological index showing a good correlation with slow-moving landslide velocity (e.g., Enokida et al, 2002), we propose an extensively applicable method for setting snowmelt-induced landslides warning periods. The target areas are three 5-km meshes in Joetsu and Myoko Cities, Niigata Prefecture, central Japan, where heavy snowfall in winter and the underlying Tertiary sedimentary rocks cause many snowmelt-induced landslides every year. We used for analyses 285 landslide cases that occurred from December to May in 1979 to 2020 reported in data set on landslides compiled by the Niigata Prefectural government. We used  (meltwater and/or rainwater), which is the total amount of water reaching the ground surface, instead of precipitation, for calculating the antecedent precipitation index. The amount of snowmelt was estimated based on the heat balance method using the Japan Meteorological Agency observation data alone (Matsunaga, 2019) for the center of each mesh with an average elevation within the mesh.  and the antecedent  index with a various half-life were calculated hourly. Using the standard score, calculated by normalizing the antecedent  index, we determined the critical standard score capturing 70% of the target landslides in each mesh and the half-life minimizing the landslides warning periods (i.e., periods during which the standard score exceeds the critical standard score). These procedures resulted in the average landslides warning periods per year of 36 to 50 days with 36 to 318 hours of the half-life for all meshes. On the other hand, snowmelt advisories were issued for 30 days per year in average from 2013 to 2020, capturing only 36% of the target landslides. Thus, the method proposed in this study shows more than 30% higher landslide capture ratio and therefore is better than snowmelt advisories for setting snowmelt-induced landslides warning periods.</p>