scholarly journals Hybrid climate datasets from a climate data evaluation system and their impacts on hydrologic simulations for the Athabasca River basin in Canada

2019 ◽  
Author(s):  
Hyung-Il Eum ◽  
Anil Gupta
Keyword(s):  
River Basin ◽  
Evaluation System ◽  
Hydrological Modeling ◽  
Water Cycle ◽  
Climatic Conditions ◽  
Superior Performance ◽  
Climate Data ◽  
Athabasca River ◽  
The North ◽  
Spatial Coverage

Abstract. A reliable climate dataset is a backbone for modeling the essential processes of the water cycle and predicting future conditions. Although a number of gridded climate datasets are available for the North American content, which provide reasonable estimates of climatic conditions in the region, there are inherent inconsistencies in these available climate datasets (e.g., spatial and temporal varying data accuracies, meteorological parameters, length of records, spatial coverage, temporal resolution, etc). These inconsistencies raise a valid question as to which datasets are the most suitable for the study area and how to systematically combine these datasets to produce a reliable climate dataset for climate studies and hydrological modeling. This study suggested a framework, called reference reliability evaluation system (REFRES), that systematically determines a ranking of multiple climate datasets to generate a hybrid climate dataset for a region. To demonstrate the usefulness of the proposed framework, REFRES was applied to produce a historical hybrid climate dataset for the Athabasca River basin in Alberta, Canada. A proxy validation was also conducted to prove the applicability of the generated hybrid climate datasets to hydrologic simulations. This study evaluated five climate datasets, including station-based gridded climate datasets (ANUSPLIN, Alberta Township, and PNWNAmet), a multi-source gridded dataset (Canadian Precipiation Analysis – CaPA), and a reanalysis-based dataset (NARR). The results showed that the gridded climate interpolated from station data performed better than multi-source and reanalysis based climate datasets. For the Athabasca River basin, Township and ANUSPLIN were mostly ranked first for precipitation and temperature, respectively. The proxy validation also confirmed the superior performance of hybrid climate datasets compared with the other five individual climate datasets investigated in this study. These results indicate that the hybrid climate dataset provides a better representation of historical climatic conditions and thus, enhancing the reliability of hydrologic simulations.

scholarly journals Hybrid climate datasets from a climate data evaluation system and their impacts on hydrologic simulations for the Athabasca River basin in Canada

2019 ◽  
Vol 23 (12) ◽  
pp. 5151-5173 ◽  
Author(s):  
Hyung-Il Eum ◽  
Anil Gupta
Keyword(s):  
River Basin ◽  
North American ◽  
Evaluation System ◽  
Water Cycle ◽  
Climatic Conditions ◽  
Climate Impacts ◽  
Climate Data ◽  
Athabasca River ◽  
The North ◽  
Spatial Coverage

Abstract. A reliable climate dataset is the backbone for modelling the essential processes of the water cycle and predicting future conditions. Although a number of gridded climate datasets are available for the North American content which provide reasonable estimates of climatic conditions in the region, there are inherent inconsistencies in these available climate datasets (e.g., spatially and temporally varying data accuracies, meteorological parameters, lengths of records, spatial coverage, temporal resolution, etc.). These inconsistencies raise questions as to which datasets are the most suitable for the study area and how to systematically combine these datasets to produce a reliable climate dataset for climate studies and hydrological modelling. This study suggests a framework called the REFerence Reliability Evaluation System (REFRES) that systematically ranks multiple climate datasets to generate a hybrid climate dataset for a region. To demonstrate the usefulness of the proposed framework, REFRES was applied to produce a historical hybrid climate dataset for the Athabasca River basin (ARB) in Alberta, Canada. A proxy validation was also conducted to prove the applicability of the generated hybrid climate datasets to hydrologic simulations. This study evaluated five climate datasets, including the station-based gridded climate datasets ANUSPLIN (Australia National University Spline), Alberta Township, and the Pacific Climate Impacts Consortium's (PCIC) PNWNAmet (PCIC NorthWest North America meteorological dataset), a multi-source gridded dataset (Canadian Precipitation Analysis; CaPA), and a reanalysis-based dataset (North American Regional Reanalysis; NARR). The results showed that the gridded climate interpolated from station data performed better than multi-source- and reanalysis-based climate datasets. For the Athabasca River basin, Township and ANUSPLIN were ranked first for precipitation and temperature, respectively. The proxy validation also confirmed the utility of hybrid climate datasets in hydrologic simulations compared with the other five individual climate datasets investigated in this study. These results indicate that the hybrid climate dataset provides the best representation of historical climatic conditions and, thus, enhances the reliability of hydrologic simulations.

scholarly journals Status of Major Glaciers of Hunza River Basin, Under Changing Climatic Conditions of Pakistan Over the Period of (1990-2018)

2021 ◽  
Author(s):  
Sajid Ali ◽  
Garee Khan ◽  
Wajid Hassan ◽  
Javed Akhter Qureshi ◽  
Iram Bano
Keyword(s):  
River Basin ◽  
Primary Source ◽  
Climatic Conditions ◽  
Climate Data ◽  
Outburst Floods ◽  
Glacial Lake Outburst Floods ◽  
Digital Elevation ◽  
Remote Sensing Techniques ◽  
Elevation Model ◽  
Hunza River

Abstract Ice masses and snow of Hunza River Basin (HRB) are an important primary source of fresh water and lifeline for downstream inhabitants. Changing climatic conditions seriously put an impact on these available ice and snow masses. These glaciers may affect downstream population by glacial lake outburst floods (GLOF) and surge events due to climatic variation. So, monitoring of these glaciers and available ice masses are important. This research delivers an approach for selected glaciers of the Hunza river basin. An attempt is made in this study using Landsat (OLI, ETM, ETM+, TM), digital elevation model (DEM), Geographic Information System and Remote Sensing techniques (RS&GIS) techniques. We delineated 27 glaciers within HRB from the period of 1990-2018. These glaciers' total area is about 2589.75 ±86km 2 in 1990 and about 2565.12 ±68km 2 in 2018. Our results revealed that from 2009 to 2015, glacier coverage of HRB advanced with a mean annual advance rate of 2.22±0.1 km 2 a -1 . Conversely, from 1994 to 1999, the strongest reduction in glacier area with a mean rate of - 3.126±0.3km 2 a -1 is recorded. The glaciers of HRB are relatively stable compared to Hindukush, Himalayan and Tibetan Plateau (TP) region of the world. The steep slope glacier's retreat rate is more than that of gentle slope glaciers, and the glaciers below elevation of 5000 m above sea level change significantly. Based on climate data from 1995-2018, HRB shows a decreasing trend in temperature and increasing precipitation. The glacier area's overall retreat is due to an increase in summer temperature while the glacier advancement is induced possibly by winter and autumn precipitation.

scholarly journals Assessment of TMPA 3B42V7 and PERSIANN-CDR in Driving Hydrological Modeling in a Semi-Humid Watershed in Northeastern China

2020 ◽  
Vol 12 (19) ◽  
pp. 3133
Author(s):  
Lu Zhang ◽  
Zhuohang Xin ◽  
Huicheng Zhou
Keyword(s):  
Hydrological Modeling ◽  
Assessment Tool ◽  
Surface Flow ◽  
Northeastern China ◽  
Base Flow ◽  
Hydrological Processes ◽  
Superior Performance ◽  
Climate Data ◽  
Satellite Precipitation ◽  
Precipitation Estimation

Recent developments of satellite precipitation products provide an unprecedented opportunity for better precipitation estimation, and thus broaden hydrological application. However, due to the errors and uncertainties of satellite products, a thorough validation is usually required before putting into the real hydrological application. As such, this study aims to provide a comprehensive evaluation on the performances of Tropical Rainfall Measuring Mission Multi-satellite Precipitation Analysis (TMPA) 3B42V7 and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR), as well as their adequacies in simulating hydrological processes in a semi-humid region in the northeastern China. It was found that TMPA 3B42V7 showed a superior performance at the daily and monthly time scales, and had a favorable capture of the rainfall-intensity distribution. Intra-annual comparisons indicated a better representation of TMPA 3B42V7 from January to September, whereas PERSIANN-CDR was more reliable from October to December. The Soil and Water Assessment Tool (SWAT) driven by gauge precipitation data performed excellently with NSE > 0.9, while the performances of TMPA 3B42V7- and PERSIANN-CDR-based models are satisfactory with NSE > 0.5. The performances varied under different flow levels and hydrological years. Water balance analysis indicated a better performance of TMPA 3B42V7 in simulating the hydrological processes, including evapotranspiration, groundwater recharge and total runoff. The runoff compositions (i.e., base flow, subsurface flow, and surface flow) driven by TMPA 3B42V7 were more accordant with the actual hydrological features. This study will not only help recognize the potential satellite precipitation products for local water resources management, but also be a reference for the poor-gauged regions with similar hydrologic and climatic conditions around the world, especially the northeastern China and western Russia.

scholarly journals Determination of ecological conditions and geographical distribution of vegetation in the Goksu basin

2022 ◽  
pp. 51-61
Author(s):  
Fatih Karaosmanoglu
Keyword(s):  
Geographical Distribution ◽  
Plant Species ◽  
Geographical Area ◽  
Climatic Conditions ◽  
Physical Factors ◽  
Climate Data ◽  
The South ◽  
Climate Type ◽  
Ecological Conditions ◽  
The North

On the ecological conditions and distribution of vegetation in any geographical area; The mutual interaction of factors such as climate (temperature-precipitation), topography (altitude-mountain extent), soil plays an important role. In addition, these factors also determine the ecological and geographical distribution of vegetation at micro and macro levels. In this study, geographic information systems (GIS) are used as a method and here; Digital elevation model of the basin (30x30), multi-year climate data (precipitation, temperature), Erinc climate type results, soil distribution, stand distribution, plant profiles and field photographs are the materials used in the study. By processing these data, the type and distribution of vegetation in the Goksu basin were determined. According to these findings, physical factors such as altitude and the extent of the mountains have created significant differences in the precipitation and temperature distribution of the basin. This difference was clearly observed in the Erinc climate classification results, and the south of the basin presented humid and semihumid climate characteristics, and the north presented semi-arid climate characteristics. These climatic conditions also affected the soil formation and type,causing a wide distribution of non-calcareous brown soils and non-calcareous brown forest soils in the field. As a result of all these conditions, plant species showed different vertical and spatial distribution. In the part from the south of the basin to Saimbeyli, plant species such maquis, pinus brutia, pinus nigra, Cedrus libani, Abies, Juniperus are distributed, while in the north, oak species such as oak, Bromus torhentallus, Astragalus, Thymus have been distributed. Thus, factors such as climate, topography and soil played an important role in the spread of vegetation and species in the Goksu Basin.

scholarly journals Comparison Study of Multiple Precipitation Forcing Data on Hydrological Modeling and Projection in the Qujiang River Basin

Water ◽  
2020 ◽  
Vol 12 (9) ◽  
pp. 2626 ◽  
Author(s):  
Yongyu Song ◽  
Jing Zhang ◽  
Xianyong Meng ◽  
Yuyan Zhou ◽  
Yuequn Lai ◽  
...  
Keyword(s):  
River Basin ◽  
Hydrological Modeling ◽  
Assessment Tool ◽  
Water Cycle ◽  
Swat Model ◽  
Precipitation Data ◽  
Simulation Performance ◽  
Surface Water Resources ◽  
Monthly Streamflow ◽  
Key Factor

As a key factor in the water cycle and climate change, the quality of precipitation data directly affects the hydrological processes of the river basin. Although many precipitation products with high spatial and temporal resolutions are now widely used, it is meaningful and necessary to investigate and evaluate their merits and demerits in hydrological applications. In this study, two satellite-based precipitation products (Tropical Rainfall Measurement Mission, TRMM; Integrated Multi-satellite Retrievals for GPM, IMERG) and one reanalysis precipitation product (China Meteorological Assimilation Driving Datasets for the Soil and Water Assessment Tool (SWAT) model, CMADS) are studied to compare their streamflow simulation performance in the Qujiang River Basin, China, using the SWAT model with gauged rainfall data as a reference. The main conclusions are as follows: (1) CMADS has stronger precipitation detection capabilities compared to gauged rainfall, while TRMM results in the most obvious overestimation in the four sub-basins. (2) In daily and monthly streamflow simulations, CMADS + SWAT mode offers the best performance. CMADS and IMERG can provide high quality precipitation data for data-scarce areas, and IMERG can effectively avoid the overestimation of streamflow caused by TRMM, especially on a daily scale. (3) The runoff projections of the three modes under RCP (Representative Concentration Pathway) 4.5 was higher than that of RCP 8.5 on the whole. IMERG + SWAT overestimates the surface water resources of the basin compared to CMADS + SWAT, while TRMM + SWAT provides the most stable uncertainty. These findings contribute to the comparison of the differences among the three precipitation products and provides a reference for the selection of precipitation data in similar regions.

scholarly journals Evaluation and Calibration of Alternative Methods for Estimating Reference Evapotranspiration in the Senegal River Basin

Hydrology ◽  
2020 ◽  
Vol 7 (2) ◽  
pp. 24 ◽  
Author(s):  
Papa Malick Ndiaye ◽  
Ansoumana Bodian ◽  
Lamine Diop ◽  
Abdoulaye Deme ◽  
Alain Dezetter ◽  
...  
Keyword(s):  
River Basin ◽  
Hydrological Modeling ◽  
Reference Evapotranspiration ◽  
Water Cycle ◽  
Irrigation Management ◽  
Alternative Methods ◽  
Coefficient Of Determination ◽  
Climate Variables ◽  
Senegal River ◽  
Senegal River Basin

Reference evapotranspiration (ET0) is a key element of the water cycle in tropical areas for the planning and management of water resources, hydrological modeling, and irrigation management. The objective of this research is to assess twenty methods in computing ET0 in the Senegal River Basin and to calibrate and validate the best methods that integrate fewer climate variables. The performance of alternative methods compared to the Penman Monteith (FAO56-PM) model is evaluated using the coefficient of determination (R2), normalized root mean square error (NRMSE), percentage of bias (PBIAS), and the Kling–Gupta Efficiency (KGE). The most robust methods integrating fewer climate variables were calibrated and validated and the results show that Trabert, Valiantzas 2, Valiantzas 3, and Hargreaves and Samani models are, respectively, the most robust for ET0 estimation. The calibration improves the estimates of reference evapotranspiration compared to original models. It improved the performance of these models with an increase in KGE values of 45%, 32%, 29%, and 19% for Trabert, Valiantzas 2, Valiantzas 3, and Hargreaves and Samani models, respectively. From a spatial point of view, the calibrated models of Trabert and Valiantzas 2 are robust in all the climatic zones of the Senegal River Basin, whereas, those of Valiantzas 3 and Hargreaves and Samani are more efficient in the Guinean zone. This study provides information on the choice of a model for estimating evapotranspiration in the Senegal River Basin.

scholarly journals The China Meteorological Assimilation Driving Datasets for the SWAT Model (CMADS) Application in China: A Case Study in Heihe River Basin

2017 ◽  
Author(s):  
Xian-yong Meng ◽  
Hao Wang ◽  
Si-yu Cai ◽  
Xue-song Zhang ◽  
Guo-yong Leng ◽  
...  
Keyword(s):  
River Basin ◽  
Large Scale ◽  
Hydrologic Modeling ◽  
Hydrological Modeling ◽  
Assessment Tool ◽  
Swat Model ◽  
Heihe River Basin ◽  
Heihe River ◽  
Climate Data ◽  
Weather Stations

Large-scale hydrological modeling in China is challenging given the sparse meteorological stations and large uncertainties associated with atmospheric forcing data.Here we introduce the development and use of the China Meteorological Assimilation Driving Datasets for the SWAT model (CMADS) in the Heihe River Basin(HRB) for improving hydrologic modeling, by leveraging the datasets from the China Meteorological Administration Land Data Assimilation System (CLDAS)(including climate data from nearly 40000 area encryption stations, 2700 national automatic weather stations, FengYun (FY) 2 satellite and radar stations). CMADS uses the Space Time Multiscale Analysis System (STMAS) to fuse data based on ECWMF ambient field and ensure data accuracy. In addition, compared with CLDAS, CMADS includes relative humidity and climate data of varied resolutions to drive hydrological models such as the Soil and Water Assessment Tool (SWAT) model. Here, we compared climate data from CMADS, Climate Forecast System Reanalysis (CFSR) and traditional weather station (TWS) climate forcing data and evaluatedtheir applicability for driving large scale hydrologic modeling with SWAT. In general, CMADS has higher accuracy than CFRS when evaluated against observations at TWS; CMADS also provides spatially continuous climate field to drive distributed hydrologic models, which is an important advantage over TWS climate data, particular in regions with sparse weather stations. Therefore, SWAT model simulations driven with CMADS and TWS achieved similar performances in terms of monthly and daily stream flow simulations, and both of them outperformed CFRS. For example, for the three hydrological stations (Ying Luoxia, Qilian Mountain, and ZhaMasheke) in the HRB at the monthly and daily Nash-Sutcliffe efficiency ranges of 0.75-0.95 and 0.58-0.78, respectively, which are much higher than corresponding efficiency statistics achieved with CFSR (monthly: 0.32-0.49 and daily: 0.26 – 0.45). The CMADS dataset is available free of charge and is expected to a valuable addition to the existing climate reanalysis datasets for deriving distributed hydrologic modeling in China and other countries in East Asia.

Impacts of climatic seasonality on weathering of rock-cut structures at Lalibela, Ethiopia

2021 ◽  
Author(s):  
Blen Taye ◽  
Heather Viles
Keyword(s):  
Climatic Conditions ◽  
Rock Surface ◽  
Salt Weathering ◽  
Northern Ethiopia ◽  
Climate Data ◽  
Climate Trends ◽  
Weathering Processes ◽  
North East ◽  
The North ◽  
Diurnal Range

<p>Weathering of rock-cut structures exposed to the environment is strongly influenced by fluctuations in climatic variables. Both macro and microclimate data are needed to identify key weathering types and rates likely to affect rock-cut structures in a specific region. The aim of this paper is to study the macro and micro climatic conditions affecting the rock-cut churches in Lalibela, Ethiopia to determine how the climate influences weathering at this site. Macro climate data collected over a 26-year period and microclimate data monitored on the north, east, south and west walls at one of the churches in the Lalibela church complex (Bete Mariam) are used to make these assessments. Microclimate data was monitored during the long rains (Kiremt), short rains (Belg) and dry (Bega) seasons in 2018 and 2019. The results showed a high seasonal variation in macro climatic conditions like rainfall and ambient relative humidity. The micro climatic (rock surface) conditions also tended to vary seasonally. The diurnal range of rock surface temperature during Bega varied significantly depending on which cardinal directions the walls were facing, with south and west facing walls having high diurnal thermal ranges. The influence of aspect was less pronounced in Belg and Kiremt, but cloud cover played an important role in varying the range of diurnal thermal and humidity cycles from day to day during these seasons. These climate trends are likely to cause seasonal variations in wetting and drying cycles, deep wetting, increased time of wetness and thermal cycling. These wetting/drying and heating/cooling characteristics affect weathering processes. During Kiremt, biological weathering, salt weathering and clay swelling are more likely to occur than in Belg and Bega. High diurnal thermal ranges in Bega are likely to cause thermal fatigue in this season. This is the first paper to address the macro and micro climatic trends that influence rock weathering at the rock-cut churches in Lalibela. The results of this study also have implications for rock-cut structures in northern Ethiopia having similar environmental conditions as Lalibela.</p>

scholarly journals Multi-Structure Hydrological Ensemble to Improve Flow Daily Prediction in the Sumapaz River Basin, Colombia

10.29007/l1l2 ◽  
2018 ◽  
Author(s):  
Pedro Arboleda-Obando ◽  
David Zamora ◽  
Carolina Vega ◽  
Nicolás Duque ◽  
Erasmo Rodriguez
Keyword(s):  
Uncertainty Analysis ◽  
River Basin ◽  
Bayesian Model Averaging ◽  
Water Cycle ◽  
Model Averaging ◽  
Climatic Conditions ◽  
Performance Criteria ◽  
And Performance ◽  
The Individual ◽  
Prediction And Forecasting

Hydrological ensembles have gained importance for prediction and forecasting in water cycle variables. In spite of this, the relevance of the individual models in the ensemble is not usually established, in terms of the ensemble structure (i.e. their members) and the performance this structure exhibits through different climatic conditions (intrannual variability, for example). This analysis accounts for the uncertainty in the structure of the models and their responses (e.g. outputs), in comparison to the observed data. In this regard, the research here described attempts to determine the incidence of the ensemble members built for each month of the year, in the prediction of daily flows, through the use of the Bayesian Model Averaging (BMA) method. Moreover, using BMA calibrated parameters as inputs, an uncertainty analysis is carried out for the calibration period, and in monthly average terms, obtaining finer uncertainty bounds. This analysis was implemented in the Sumapaz River basin, part of the Magdalena Cauca Macro- Basin (MCMB) in Colombia. Results showed differences in ensemble structures and performance according to its original performance criteria, and better results when using a monthly BMA for the uncertainty analysis.

scholarly journals The China Meteorological Assimilation Driving Datasets for the SWAT model (CMADS) and it's Application in the Heihe River Basin in China

2016 ◽  
Author(s):  
Hao Wang ◽  
Xian-yong Meng ◽  
Si-yu Cai ◽  
Xue-song Zhang ◽  
Xiao-hui Lei ◽  
...  
Keyword(s):  
River Basin ◽  
Large Scale ◽  
Hydrologic Modeling ◽  
Hydrological Modeling ◽  
Swat Model ◽  
Heihe River Basin ◽  
Heihe River ◽  
Climate Data ◽  
Weather Stations ◽  
The Heihe River Basin

Large-scale hydrological modeling in China is challenging given the sparse meteorological stations and large uncertainties associated with atmospheric forcing data. Here we introduce the development and use of the China Meteorological Assimilation Driving Datasets for the SWAT model (CMADS) in the Heihe River Basin(HRB) for improving hydrologic modeling, by leveraging the datasets from the China Meteorological Administration Land Data Assimilation System (CLDAS)(including climate data from nearly 40000 area encryption stations, 2700 national automatic weather stations, FengYun (FY) 2 satellite and radar stations). CMADS uses the Space Time Multiscale Analysis System (STMAS) to fuse data based on ECWMF ambient field and ensure data accuracy. In addition, compared with CLDAS, CMADS includes relative humidity and climate data of varied resolutions to drive hydrological models such as the Soil and Water Assessment Tool (SWAT) model. Here, we compared climate data from CMADS, Climate Forecast System Reanalysis (CFSR) and traditional weather station (TWS) climate forcing data and evaluated their applicability for driving large scale hydrologic modeling with SWAT. In general, CMADS has higher accuracy than CFRS when evaluated against observations at TWS; CMADS also provides spatially continuous climate field to drive distributed hydrologic models, which is an important advantage over TWS climate data, particular in regions with sparse weather stations. Therefore, SWAT model simulations driven with CMADS and TWS achieved similar performances in terms of monthly and daily stream flow simulations, and both of them outperformed CFRS. For example, for the three hydrological stations (Ying Luoxia, Qilian Mountain, and ZhaMasheke) in the HRB at the monthly and daily Nash-Sutcliffe efficiency ranges of 0.75-0.95 and 0.58-0.78, respectively, which are much higher than corresponding efficiency statistics achieved with CFSR (monthly: 0.32-0.49 and daily: 0.26 – 0.45). The CMADS dataset is available free of charge and is expected to a valuable addition to the existing climate reanalysis datasets for deriving distributed hydrologic modeling in China and other countries in East Asia.

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