scholarly journals Regional drought assessment using a distributed hydrological model coupled with Standardized Runoff Index

2015 ◽  
Vol 368 ◽  
pp. 397-402
Author(s):  
H. Shen ◽  
F. Yuan ◽  
L. Ren ◽  
M. Ma ◽  
H. Kong ◽  
...  
Keyword(s):  
Large Scale ◽  
Hydrological Model ◽  
Coupled Model ◽  
Temporal Variations ◽  
Distributed Hydrological Model ◽  
Drought Forecasting ◽  
Infiltration Capacity ◽  
Drought Assessment ◽  
Regional Drought ◽  
Standardized Runoff Index

Abstract. Drought assessment is essential for coping with frequent droughts nowadays. Owing to the large spatio-temporal variations in hydrometeorology in most regions in China, it is very necessary to use a physically-based hydrological model to produce rational spatial and temporal distributions of hydro-meteorological variables for drought assessment. In this study, the large-scale distributed hydrological model Variable Infiltration Capacity (VIC) was coupled with a modified standardized runoff index (SRI) for drought assessment in the Weihe River basin, northwest China. The result indicates that the coupled model is capable of reasonably reproducing the spatial distribution of drought occurrence. It reflected the spatial heterogeneity of regional drought and improved the physical mechanism of SRI. This model also has potential for drought forecasting, early warning and mitigation, given that accurate meteorological forcing data are available.

scholarly journals Comparative performance evaluation of self-adaptive differential evolution with GA, SCE and DE algorithms for the automatic calibration of a computationally intensive distributed hydrological model

2020 ◽  
Author(s):  
Saswata Nandi ◽  
M. Janga Reddy
Keyword(s):  
Differential Evolution ◽  
Hydrological Model ◽  
Automatic Calibration ◽  
Hydrological Models ◽  
Distributed Hydrological Model ◽  
Infiltration Capacity ◽  
Vic Model ◽  
Adaptive Differential Evolution ◽  
Computationally Intensive ◽  
Self Adaptive

Abstract Recently, physically-based hydrological models have been gaining much popularity in various activities of water resources planning and management, such as assessment of basin water availability, floods, droughts, and reservoir operation. Every hydrological model contains some parameters that must be tuned to the catchment being studied to obtain reliable estimates from the model. This study evaluated the performance of different evolutionary algorithms, namely genetic algorithm (GA), shuffled complex evolution (SCE), differential evolution (DE), and self-adaptive differential evolution (SaDE) algorithm for the parameter calibration of a computationally intensive distributed hydrological model, variable infiltration capacity (VIC) model. The methodology applied and tested for a case study of the upper Tungabhadra River basin in India, and the performance of the algorithms is evaluated in terms of reliability, variability, efficacy measures in a limited number of function evaluations, their ability for achieving global convergence, and also by their capability to produce a skillful simulation of streamflows. The results of the study indicated that SaDE facilitates an effective calibration of the VIC model with higher reliability and faster convergence to optimal solutions as compared to the other methods. Moreover, due to the simplicity of the SaDE, it provides easy implementation and flexibility for the automatic calibration of complex hydrological models.

scholarly journals Impacts of the 1900–74 Increase in Anthropogenic Aerosol Emissions from North America and Europe on Eurasian Summer Climate

2018 ◽  
Vol 31 (20) ◽  
pp. 8381-8399 ◽  
Author(s):  
S. Undorf ◽  
M. A. Bollasina ◽  
G. C. Hegerl
Keyword(s):  
Twentieth Century ◽  
Large Scale ◽  
Coupled Model ◽  
Temporal Variations ◽  
Anthropogenic Aerosols ◽  
Anthropogenic Aerosol ◽  
Near Surface ◽  
Summer Climate ◽  
Aerosol Emissions ◽  
The Impact

The impact of North American and European (NAEU) anthropogenic aerosol emissions on Eurasian summer climate during the twentieth century is studied using historical single- and all-forcing (including anthropogenic aerosols, greenhouse gases, and natural forcings) simulations from phase 5 of the Coupled Model Intercomparison Project (CMIP5). Intermodel agreement on significant linear trends during a period of increasing NAEU sulfate emissions (1900–74) reveals robust features of NAEU aerosol impact, supported by opposite changes during the subsequent period of decreasing emissions. Regionally, these include a large-scale cooling and associated anticyclonic circulation, as well as a narrowing of the diurnal temperature range (DTR) over Eurasian midlatitudes. Remotely, NAEU aerosols induce a drying over the western African and northern Indian monsoon regions and a strengthening and southward shift of the subtropical jet consistent with the pattern of temperature change. Over Europe, the temporal variations of observed temperature, pressure, and DTR tend to agree better with simulations that include aerosols. Throughout the twentieth century, aerosols are estimated to explain more than a third of the simulated interdecadal forced variability of European near-surface temperature and more than half between 1940 and 1970. These results highlight the substantial aerosol impact on Eurasian climate, already identifiable in the first half of the twentieth century. This may be relevant for understanding future patterns of change related to further emission reductions.

scholarly journals Predicting floods in a large karst river basin by coupling PERSIANN-CCS QPEs with a physically based distributed hydrological model

2019 ◽  
Vol 23 (3) ◽  
pp. 1505-1532 ◽  
Author(s):  
Ji Li ◽  
Daoxian Yuan ◽  
Jiao Liu ◽  
Yongjun Jiang ◽  
Yangbo Chen ◽  
...  
Keyword(s):  
River Basin ◽  
Relative Error ◽  
Hydrological Model ◽  
Peak Flow ◽  
Coupled Model ◽  
Distributed Hydrological Model ◽  
Flood Simulation ◽  
Flood Prediction ◽  
Physically Based ◽  
Karst River

Abstract. In general, there are no long-term meteorological or hydrological data available for karst river basins. The lack of rainfall data is a great challenge that hinders the development of hydrological models. Quantitative precipitation estimates (QPEs) based on weather satellites offer a potential method by which rainfall data in karst areas could be obtained. Furthermore, coupling QPEs with a distributed hydrological model has the potential to improve the precision of flood predictions in large karst watersheds. Estimating precipitation from remotely sensed information using an artificial neural network-cloud classification system (PERSIANN-CCS) is a type of QPE technology based on satellites that has achieved broad research results worldwide. However, only a few studies on PERSIANN-CCS QPEs have occurred in large karst basins, and the accuracy is generally poor in terms of practical applications. This paper studied the feasibility of coupling a fully physically based distributed hydrological model, i.e., the Liuxihe model, with PERSIANN-CCS QPEs for predicting floods in a large river basin, i.e., the Liujiang karst river basin, which has a watershed area of 58 270 km2, in southern China. The model structure and function require further refinement to suit the karst basins. For instance, the sub-basins in this paper are divided into many karst hydrology response units (KHRUs) to ensure that the model structure is adequately refined for karst areas. In addition, the convergence of the underground runoff calculation method within the original Liuxihe model is changed to suit the karst water-bearing media, and the Muskingum routing method is used in the model to calculate the underground runoff in this study. Additionally, the epikarst zone, as a distinctive structure of the KHRU, is carefully considered in the model. The result of the QPEs shows that compared with the observed precipitation measured by a rain gauge, the distribution of precipitation predicted by the PERSIANN-CCS QPEs was very similar. However, the quantity of precipitation predicted by the PERSIANN-CCS QPEs was smaller. A post-processing method is proposed to revise the products of the PERSIANN-CCS QPEs. The karst flood simulation results show that coupling the post-processed PERSIANN-CCS QPEs with the Liuxihe model has a better performance relative to the result based on the initial PERSIANN-CCS QPEs. Moreover, the performance of the coupled model largely improves with parameter re-optimization via the post-processed PERSIANN-CCS QPEs. The average values of the six evaluation indices change as follows: the Nash–Sutcliffe coefficient increases by 14 %, the correlation coefficient increases by 15 %, the process relative error decreases by 8 %, the peak flow relative error decreases by 18 %, the water balance coefficient increases by 8 %, and the peak flow time error displays a 5 h decrease. Among these parameters, the peak flow relative error shows the greatest improvement; thus, these parameters are of the greatest concern for flood prediction. The rational flood simulation results from the coupled model provide a great practical application prospect for flood prediction in large karst river basins.

Simulating the impacts of land-use change on a large-scale runoff using the easy distributed hydrological model

2014 ◽  
Vol 21 (2) ◽  
pp. 122-134
Author(s):  
Xiaomin Huang ◽  
Weihong Liao ◽  
Xiaohui Lei ◽  
Yuhui Wang ◽  
Yunzhong Jiang ◽  
...  
Keyword(s):  
Land Use ◽  
Land Use Change ◽  
Large Scale ◽  
Hydrological Model ◽  
Distributed Hydrological Model

scholarly journals Effect of spatial distribution of daily rainfall on interior catchment response of a distributed hydrological model

2007 ◽  
Vol 11 (2) ◽  
pp. 677-693 ◽  
Author(s):  
J. M. Schuurmans ◽  
M. F. P. Bierkens
Keyword(s):  
Soil Moisture ◽  
Spatial Variability ◽  
Groundwater Level ◽  
Large Scale ◽  
Hydrological Model ◽  
Daily Rainfall ◽  
Distributed Hydrological Model ◽  
General Behaviour ◽  
Average Rainfall ◽  
Insight Into

Abstract. We investigate the effect of spatial variability of daily rainfall on soil moisture, groundwater level and discharge using a physically-based, fully-distributed hydrological model. This model is currently in use with the district water board and is considered to represent reality. We focus on the effect of rainfall spatial variability on day-to-day variability of the interior catchment response, as well as on its effect on the general hydrological behaviour of the catchment. The study is performed in a flat rural catchment (135 km2) in the Netherlands, where the climate is semi-humid (average precipitation 800 mm/year, evapotranspiration 550 mm/year) and rainfall is predominantly stratiform (i.e. large scale). Both range-corrected radar data (resolution 2.5×2.5 km2) as well as data from a dense network of 30 raingauges are used, observed for the period March–October 2004. Eight different rainfall scenarios, either spatially distributed or spatially uniform, are used as input for the hydrological model. The main conclusions from this study are: (i) using a single raingauge as rainfall input carries a great risk for the prediction of discharge, groundwater level and soil moisture, especially if the raingauge is situated outside the catchment; (ii) taking into account the spatial variability of rainfall instead of using areal average rainfall as input for the model is needed to get insight into the day-to-day spatial variability of discharge, groundwater level and soil moisture content; (iii) to get insight into the general behaviour of the hydrological system it is sufficient to use correct predictions of areal average rainfall over the catchment.

Comparing 18 precipitation datasets for large scale distributed hydrological modelling

2020 ◽  
Author(s):  
Luigia Brandimarte ◽  
Maurizio Mazzoleni ◽  
Alessandro Amaranto
Keyword(s):  
High Performance ◽  
Large Scale ◽  
Hydrological Model ◽  
River Basins ◽  
Distributed Hydrological Model ◽  
Distributed Models ◽  
Distributed Modelling ◽  
Human Footprint ◽  
Class 1 ◽  
Precipitation Gauge

<p>Our understanding of the advantages and limitations of satellite derived precipitation datasets as a forcing to hydrological models has made tremendous progress over the past decade. However, most studies have only analysed the performance of one or few datasets, have used global precipitation datasets to force lumped models on regional/large-scale basins, or have adopted more complex distributed models but applied them to small basin scales.</p><p>We aimed at addressing these gaps in the literature: in particular, we compared the performance of 18 different precipitation datasets used as input in a grid-based distributed hydrological model to assess streamflow in large-scale river basins. These datasets are classified as Uncorrected Satellites, Corrected Satellites, and Reanalysis-Gauges based datasets. The hydrological model is applied to 8 large scale river basins (Amazon, Brahmaputra, Congo, Danube, Godavari, Mississippi, Rhine and Volga) with different sizes, presence of hydraulic structures, human footprint, hydrometeorological characteristics, and precipitation gauge network density were selected.</p><p>The results of this study showed that there is not a unique best performing precipitation dataset for all basins and results are very sensitive to the basin characteristics. However, there are few datasets which persistently outperform the others: SM2RAIN-ASCAT for Class 1, CHIRPS V2.0, MSWEP V2.1, and CMORPH-CRTV1.0 for Class 2, GPCC and WFEDEI GPCC for Class 3. The use of a distributed modelling approach rather than lumped is supported by the fact that precipitation datasets showing the highest model result at the basin outlet do not show the same high performance at internal locations of the basin. In addition, precipitation datasets belonging to Class 2 outperform the other datasets in basins with Tropical and Temperate-Arid climate (e.g. Congo, Mississippi and Godavari), while Class 3 datasets show the highest NSE values in Temperate and Temperate-Cold basins (e.g. Danube, Rhine and Volga).</p>

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