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2 Acta Geophysica

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Mapping food extend and its impact on land use/land cover and settlements variations: a case study of Layyah District, Punjab, Pakistan

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Soomro S.-e. h. , Hu C. , Boota M. W. , Soomro M. H. A. A. , Jian S. , Zafar Z. , Li X.

tom Vol. 69, no. 6

2291--2304

Floods are the most frequent and most distractive natural disaster around the globe. Pakistan is facing frequent flooding since 1929 and foods in the Indus river basin cost more than 7000 lives and caused mighty changes in land use and land covers (LULC) since 1947. District Layyah hit by food on August 1, 2010. Landsat ETM+ with 30 m spatial resolution was utilized to investigate the LULC changes in district Layyah for the 2010 food. It was revealed water area increased 8.05% from July 3 (379.13 km2 ) to August 20 (656.02 km2 ) in district Layyah. Vegetation cover increased from 1149.62 km2 on July 3 to 1842.23 km2 on August 20 in district Layyah and showed a 20.13% increment. Barren/built-up area showed a decrement of 28.18% from 1911.72 km2 in pre-food analysis to 941.90 km2 in the post-food analysis. Total 15 union councils (UC) of district Layyah were affected by food from which 10 lies in tehsil Layyah and 5 belongs to tehsil Karor Lal Esan. Flood affects 177 settlements in district Layyah from which 156 belong to tehsil Layyah and 21 were from tehsil Karor Lal Esan. These results suggest that the impacts of the food on LULC need more attention to cope with the challenge of frequent flooding and impacts in Pakistan.

Comparison of different ensemble precipitation forecast system evaluation, integration and hydrological applications

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Tang Y. , Wu Q. , Soomro S.-e. h. , Li X. , Sun Y. , Hu C.

tom Vol. 71, no. 1

405--421

The examination and integration of numerical forecast products are essential for using and developing numerical forecasts and hydrological forecasts. In this paper, the control forecast products from 2010 to 2014 of four model data (China Meteorological Administration (CMA), the National Centers for Environmental Prediction (NCEP), the European Centre for Medium-Range Weather Forecasts (ECMWF), and the United Kingdom Meteorological Office (UKMO)) from The Interactive Grand Global Ensemble (TIGGE) data center were evaluated comprehensively. On this basis, a study of runoff forecasting based on multi-model (multiple regression (MR), random forest (RF), and convolutional neural network-gradient boosting decision tree (CNN-GBDT)) precipitation integration is carried out. The results show that the CMA model performs the worst, while the other models have their advantages and disadvantages in different evaluation indexes. Compared with the single-index optimal model, CMA model had a higher root-mean-square error (RMSE) of 18.4%, and a lower determination coefficient (R2 ) of 14.7%, respectively. The integration of multiple numerical forecast information is better than that of a single model, and CNN-GBDT method is superior to the multiple regression method and random forest method in improving the precision of rainfall forecast. Compared with the original model, the RMSE decreases by 13.1 ~27.9%, PO decreases to 0.538 at heavy rainfall, and the R2 increases by 4~15.2%, but the degree of improvement decreases gradually with the increase in rainfall order. The method of multi-model ensemble rainfall forecasting based on a machine learning model is feasible and can improve the accuracy of short-term rainfall forecasting. The runoff forecast based on multi-model precipitation integration has been improved, and NSE increases from 0.88 to 0.935, but there is still great uncertainty about food peaks during the food season.