Spatially distributed soil losses and sediment yield: a case study of Langat watershed, Selangor, Malaysia

2021 ◽  
pp. 104742
Author(s):  
Noor Fadzilah Yusof ◽  
Tukimat Lihan ◽  
Wan Mohd Razi Idris ◽  
Zulfahmi Ali Rahman ◽  
Muzneena Ahmad Mustapha ◽  
...  
Keyword(s):  
Sediment Yield ◽  
Spatially Distributed ◽  
Soil Losses

Modelling spatially distributed soil losses and sediment yield in the upper Grande River Basin - Brazil

CATENA ◽  
2017 ◽  
Vol 157 ◽  
pp. 139-150 ◽  
Author(s):  
Pedro Velloso Gomes Batista ◽  
Marx Leandro Naves Silva ◽  
Bárbara Pereira Christofaro Silva ◽  
Nilton Curi ◽  
Inácio Thomaz Bueno ◽  
...  
Keyword(s):  
River Basin ◽  
Sediment Yield ◽  
Spatially Distributed ◽  
Soil Losses

Epidemic waves in relief against a structured climatic background

2021 ◽  
Author(s):  
Peter Carl
Keyword(s):  
Climate Modeling ◽  
Sunshine Duration ◽  
Climate Data ◽  
Data Set ◽  
Seir Model ◽  
Epidemic Curve ◽  
Surface Climate ◽  
Spatially Distributed

<p>For directly transmissible infectious diseases, seasonality in the course of epidemics may manifest in four major ways: susceptibility of the hosts, their individual and collective behavior, transmissibility of the pathogen, and survival of the latter under evolving environmental conditions. Mechanisms and concepts are not finally settled, notably in a pandemic setting. Climatic seasonality by itself is an aggregate, structured phenomenon that provides a spatially distributed background to the epidemic outbreak and its evolution at multiple timescales. Using advanced methods of data and systems analysis, including epidemiological and climate modeling, the RKI data of the COVID-19 epidemic curve for Berlin and a five-parameter climate data set of the nearby station Lindenberg (Mark) are analyzed in daily resolution over the period March 2020 to October 2021. Aimed to identify extrinsic impacts due to organized intraseasonal climate dynamics, as seen in sunshine duration and surface climate (pressure, temperature, humidity, wind), on intrinsic dynamics of the epidemic system, an established (SEIR) model of the latter and modifications thereof have been subjected to in-depth studies with a view on both genesis and timing of epidemic waves and their potential synchronization with climatic background dynamics. Starting with a case study for the spring 2020 period of shutdown, which unveils remarkable synchronies with the seasonal transition, estimates are given and applied to the follow-up period of individual and combined impacts of climate variables on the SEIR model in different oscillatory (non-equilibrium, lately endemic) regimes of operation.</p>

scholarly journals Effect of Land Use Land Cover and Climate Change on River Flow and Soil Loss in Didessa River Basin, South West Blue Nile, Ethiopia

Hydrology ◽  
2018 ◽  
Vol 6 (1) ◽  
pp. 2 ◽  
Author(s):  
Kinati Chimdessa ◽  
Shoeb Quraishi ◽  
Asfaw Kebede ◽  
Tena Alamirew
Keyword(s):  
Climate Change ◽  
Land Use ◽  
Land Cover ◽  
River Basin ◽  
Sediment Yield ◽  
Soil Loss ◽  
River Flow ◽  
Land Use Land Cover ◽  
The Impact ◽  
Soil Losses

In the Didessa river basin, which is found in Ethiopia, the human population number is increasing at an alarming rate. The conversion of forests, shrub and grasslands into cropland has increased in parallel with the population increase. The land use/land cover change (LULCC) that has been undertaken in the river basin combined with climate change may have affected the Didessa river flow and soil loss. Therefore, this study was designed to assess the impact of LULCC on the Didessa river flow and soil loss under historical and future climates. Land use/land cover (LULC) of the years 1986, 2001 and 2015 were independently combined with the historical climate to assess their individual impacts on river flow and soil loss. Further, the impact of future climates under Representative Concentration Pathways (RCP2.6, RCP4.5 and RCP8.5) scenarios on river flow and soil loss was assessed by combining the pathways with the 2015 LULC. A physically based Soil and Water Assessment Tool (SWAT2012) model in the ArcGIS 10.4.1 interface was used to realize the purpose. Results of the study revealed that LULCC that occurred between 1986 and 2015 resulted in increased average sediment yield by 20.9 t ha−1 yr−1. Climate change under RCP2.6, RCP4.5 and RCP8.5 combined with 2015 LULC increased annual average soil losses by 31.3, 50.9 and 83.5 t ha−1 yr−1 compared with the 2015 LULC under historical climate data. It was also found that 13.4%, 47.1% and 87.0% of the total area may experience high soil loss under RCP2.6, RCP4.5 and RCP8.5, respectively. Annual soil losses of five top-priority sub catchments range from 62.8 to 57.7 per hectare. Nash Stuncliffe Simulation efficiency (NSE) and R2 values during model calibration and validation indicated good agreement between observed and simulated values both for flow and sediment yield.

scholarly journals Usage of SIMWE model to model urban overland flood: a case study in Oslo

2020 ◽  
Vol 51 (2) ◽  
pp. 366-380 ◽  
Author(s):  
Hong Li ◽  
Hongkai Gao ◽  
Yanlai Zhou ◽  
Chong-Yu Xu ◽  
Rengifo Z. Ortega M. ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
Water Depth ◽  
Large Scale ◽  
Overland Flow ◽  
Depth Maps ◽  
Physically Based ◽  
Spatially Distributed ◽  
Overland Flow Model

Abstract There has been a surge of interest in the field of urban flooding in recent years. However, current stormwater management models are often too complex to apply on a large scale. To fill this gap, we use a physically based and spatially distributed overland flow model, SIMulated Water Erosion (SIMWE). The SIMWE model requires only rainfall intensity, terrain, infiltration, and surface roughness as input. The SIMWE model has great potential for application in real-time flood forecasting. In this study, we use the SIMWE model at two resolutions (20 m and 500 m) for Oslo, and at a high resolution (1 m) at the Grefsen area, which is approximately 1.5 km2 in Oslo. The results show that the SIMWE model can generate water depth maps at both coarse and high resolutions. The spatial resolution has strong impacts on the absolute values of water depth and subsequently on the classification of flood risks. The SIMWE model at a higher spatial resolution produces more overland flow and higher estimation of flood risk with low rainfall input, but larger areas of risk with high rainfall input. The Grefsen case study shows that roads act as floodways, where overland flow accumulates and moves fast.

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