scholarly journals Comparison and Bias Correction of TMPA Precipitation Products over the Lower Part of Red–Thai Binh River Basin of Vietnam

2018 ◽  
Vol 10 (10) ◽  
pp. 1582 ◽  
Author(s):  
Hung Le ◽  
Jessica Sutton ◽  
Duong Bui ◽  
John Bolten ◽  
Venkataraman Lakshmi
Keyword(s):  
River Basin ◽  
Bias Correction ◽  
Linear Models ◽  
Dry Season ◽  
Linear Scaling ◽  
Wet Season ◽  
Ground Measurement ◽  
High Bias ◽  
Precipitation Analysis ◽  
Seasonal Analysis

As the limitation of rainfall collection by ground measurement has been widely recognized, satellite-based rainfall estimate is a promising high-resolution alternative in both time and space. This study is aimed at exploring the capacity of the satellite-based rainfall product Tropical Rainfall Measurement Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA), including 3B42V7 research data and its real-time 3B42RT data, by comparing them against data from 29 ground observation stations over the lower part of the Red–Thai Binh River Basin from March 2000 to December 2016. Various statistical metrics were applied to evaluate the TMPA products. The results showed that both 3B42V7 and 3B42RT had weak relationships with daily observations, but 3B42V7 data had strong agreement on the monthly scale compared to 3B42RT. Seasonal analysis showed that 3B42V7 and 3B42RT underestimated rainfall during the dry season and overestimated rainfall during the wet season, with high bias observed for 3B42RT. In addition, detection metrics demonstrated that TMPA products could detect rainfall events in the wet season much better than in the dry season. When rainfall intensity was analyzed, both 3B42V7 and 3B42RT overestimated the no rainfall event during the dry season but underestimated these events during the wet season. Finally, based on the moderate correlation between climatology–topography characteristics and correction factors of linear-scaling (LS) approach, a set of multiple linear models was developed to reduce the error between TMPA products and the observations. The results showed that climatology–topography-based linear-scaling approach (CTLS) significantly reduced the percentage bias (PBIAS) score and moderately improved the Nash–Sutcliffe efficiency (NSE) score. The finding of this paper gives an overview of the capacity of TMPA products in the lower part of the Red–Thai Binh River Basin regarding water resource applications and provides a simple bias correction that can be used to improve the correctness of TMPA products.

scholarly journals Bias correction of gauge-based gridded product to improve extreme precipitation analysis in the Yarlung Tsangpo-Brahmaputra River Basin

2019 ◽  
Author(s):  
Xian Luo ◽  
Xuemei Fan ◽  
Yungang Li ◽  
Xuan Ji
Keyword(s):  
River Basin ◽  
Bias Correction ◽  
Extreme Precipitation ◽  
Precipitation Data ◽  
Brahmaputra River ◽  
Spatial Coverage ◽  
Extreme Precipitation Indices ◽  
Precipitation Analysis ◽  
Precipitation Indices ◽  
Yarlung Tsangpo

Abstract. Critical gaps in the amount, quality, consistency, availability, and spatial distribution of rainfall data limit extreme precipitation analysis, and the application of gridded precipitation data are challenging because of their considerable biases. This study corrected Asian Precipitation Highly Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE) in the Yarlung Tsangpo-Brahmaputra River Basin (YBRB) using two linear and two nonlinear methods, and assessed their influence on extreme precipitation indices. The results showed that the original APHRODITE data tended to underestimate precipitation during the summer monsoon season, especially in the topographically complex Himalayan belt. Bias correction using complementary rainfall observations to add spatial coverage in data-sparse regions greatly improved the performance of extreme precipitation analysis. Although all methods could correct mean precipitation, their ability to correct the wet-day frequency and coefficient of variation were substantially different, leading to considerable differences in extreme precipitation indices. Generally, higher-skill bias-corrected APHRODITE data are expected to perform better than those corrected by lower-skill approaches. This study would provide reference for using gridded precipitation data in extreme precipitation analysis and selecting bias-corrected method for rainfall products in data-sparse regions.

scholarly journals Human-Induced Alterations to Land Use and Climate and Their Responses for Hydrology and Water Management in the Mekong River Basin

Water ◽  
2019 ◽  
Vol 11 (6) ◽  
pp. 1307 ◽  
Author(s):  
Venkataramana Sridhar ◽  
Hyunwoo Kang ◽  
Syed A. Ali
Keyword(s):  
Climate Change ◽  
Land Use ◽  
River Basin ◽  
Dry Season ◽  
Mekong River ◽  
Anthropogenic Factors ◽  
Wet Season ◽  
Industrial Growth ◽  
Mekong River Basin ◽  
Parameter Estimations

The Mekong River Basin (MRB) is one of the significant river basins in the world. For political and economic reasons, it has remained mostly in its natural condition. However, with population increases and rapid industrial growth in the Mekong region, the river has recently become a hotbed of hydropower development projects. This study evaluated these changing hydrological conditions, primarily driven by climate as well as land use and land cover change between 1992 and 2015 and into the future. A 3% increase in croplands and a 1–2% decrease in grasslands, shrublands, and forests was evident in the basin. Similarly, an increase in temperature of 1–6 °C and in precipitation of 15% was projected for 2015–2099. These natural and climate-induced changes were incorporated into two hydrological models to evaluate impacts on water budget components, particularly streamflow. Wet season flows increased by up to 10%; no significant change in dry season flows under natural conditions was evident. Anomaly in streamflows due to climate change was present in the Chiang Saen and Luang Prabang, and the remaining flow stations showed up to a 5% increase. A coefficient of variation <1 suggested no major difference in flows between the pre- and post-development of hydropower projects. The results suggested an increasing trend in streamflow without the effect of dams, while the inclusion of a few major dams resulted in decreased river streamflow of 6% to 15% possibly due to irrigation diversions and climate change. However, these estimates fall within the range of uncertainties in natural climate variability and hydrological parameter estimations. This study offers insights into the relationship between biophysical and anthropogenic factors and highlights that management of the Mekong River is critical to optimally manage increased wet season flows and decreased dry season flows and handle irrigation diversions to meet the demand for food and energy production.

scholarly journals Bias Correction of Climate Model’s Precipitation Using the Copula Method and Its Application in River Basin Simulation

Water ◽  
2019 ◽  
Vol 11 (3) ◽  
pp. 600 ◽  
Author(s):  
Georgia Lazoglou ◽  
Christina Anagnostopoulou ◽  
Charalampos Skoulikaris ◽  
Konstantia Tolika
Keyword(s):  
River Basin ◽  
Bias Correction ◽  
Climate Models ◽  
Climate Model ◽  
Monthly Precipitation ◽  
Distributed Hydrological Model ◽  
Copula Method ◽  
Corrected Data ◽  
Precipitation Analysis ◽  
Obs Data

During the last few decades, the utilization of the data from climate models in hydrological studies has increased as they can provide data in the regions that lack raw meteorological information. The data from climate models data often present biases compared to the observed data and consequently, several methods have been developed for correcting statistical biases. The present study uses the copula for modeling the dependence between the daily mean and total monthly precipitation using E-OBS data in the Mesta/Nestos river basin in order to use this relationship for the bias correction of the MPI climate model monthly precipitation. Additionally, both the non-corrected and bias corrected data are tested as they are used as the inputs to a spatial distributed hydrological model for simulating the basin runoff. The results showed that the MPI model significantly overestimates the E-OBS data while the differences are reduced sufficiently after the bias correction. The outputs from the hydrological models were proven to coincide with the precipitation analysis results and hence, the simulated discharges in the case of copula corrected data present an increased correlation with the observed flows.

scholarly journals Deriving the Reservoir Conditions for Better Water Resource Management Using Satellite-Based Earth Observations in the Lower Mekong River Basin

2019 ◽  
Vol 11 (23) ◽  
pp. 2872 ◽  
Author(s):  
Syed A. Ali ◽  
Venkataramana Sridhar
Keyword(s):  
Remote Sensing ◽  
River Basin ◽  
Water Resource Management ◽  
Large Population ◽  
Dry Season ◽  
Water Diversion ◽  
Mekong River ◽  
Landsat 8 ◽  
Wet Season ◽  
Mekong River Basin

The Mekong River basin supported a large population and ecosystem with abundant water and nutrient supply. However, the impoundments in the river can substantially alter the flow downstream and its timing. Using limited observations, this study demonstrated an approach to derive dam characteristics, including storage and flow rate, from remote-sensing-based data. Global Reservoir and Lake Monitor (GRLM), River-Lake Hydrology (RLH), and ICESat-GLAS, which generated altimetry from Jason series and inundation areas from Landsat 8, were used to estimate the reservoir surface area and change in storage over time. The inflow simulated by the variable infiltration capacity (VIC) model from 2008 to 2016 and the reservoir storage change were used in the mass balance equation to calculate outflows for three dams in the basin. Estimated reservoir total storage closely resembled the observed data, with a Nash-Sutcliffe efficiency and coefficient of determination more than 0.90 and 0.95, respectively. An average decrease of 55% in outflows was estimated during the wet season and an increase of up to 94% in the dry season for the Lam Pao. The estimated decrease in outflows during the wet season was 70% and 60% for Sirindhorn and Ubol Ratana, respectively, along with a 36% increase in the dry season for Sirindhorn. Basin-wide demand for evapotranspiration, about 935 mm, implicitly matched with the annual water diversion from 1000 to 2300 million m3. From the storage–discharge rating curves, minimum storage was also evident in the monsoon season (June–July), and it reached the highest in November. This study demonstrated the utility of remote sensing products to assess the impacts of dams on flows in the Mekong River basin.

scholarly journals Long-Term Variation of Runoff Coefficient during Dry and Wet Seasons Due to Climate Change

Water ◽  
2019 ◽  
Vol 11 (11) ◽  
pp. 2411 ◽  
Author(s):  
Ha ◽  
Ghafouri Azar ◽  
Bae
Keyword(s):  
River Basin ◽  
General Circulation ◽  
Swat Model ◽  
Dry Season ◽  
Runoff Coefficient ◽  
Wet Season ◽  
Nakdong River Basin ◽  
Increase Rate ◽  
Term Variation

This study investigates the future long-term variation of the runoff coefficient during dry and wet seasons in five major basins in South Korea. The variation is estimated from the Soil and Water Assessment Tool (SWAT) model outputs based on an ensemble of 13 different Coupled Model Intercomparison Project Phase 5 (CMIP5) general circulation models (GCMs) in representative concentration pathway (RCP) 4.5 and RCP 8.5 scenarios. The estimates show a temporal non-considerable increase rate of the runoff coefficient during the 21st century in both RCPs, in which the trend and uncertainty of the runoff coefficient in the dry season is projected as higher than that in the wet season. A sharp contrast between the trends of the two components of the runoff coefficient is found during the dry and wet seasons. Over the five major basins, a higher increase rate of runoff coefficient is projected in the northeastern part of the Han River basin and most of the area of the Nakdong River basin. The spatial variation in the runoff coefficient change also represents a relationship with the change in the percentage of each land cover/land use type over 109 subbasins, where the correlation of the wet-season runoff coefficient is calculated as higher than that of the dry season. This relationship is expected to vary with changes in temperature and precipitation during both seasons in three future periods.

scholarly journals Twentieth and Twenty-First Century Water Storage Changes in the Nile River Basin from GRACE/GRACE-FO and Modeling

2021 ◽  
Vol 13 (5) ◽  
pp. 953
Author(s):  
Emad Hasan ◽  
Aondover Tarhule ◽  
Pierre-Emmanuel Kirstetter
Keyword(s):  
River Basin ◽  
Land Surface ◽  
Water Storage ◽  
Model Performance ◽  
Dry Season ◽  
Nile River ◽  
Surface Model ◽  
Wet Season ◽  
Distribution Fitting ◽  
Nile River Basin

This research assesses the changes in total water storage (TWS) during the twentieth century and future projections in the Nile River Basin (NRB) via TWSA (TWS anomalies) records from GRACE (Gravity Recovery and Climate Experiment), GRACE-FO (Follow-On), data-driven-reanalysis TWSA and a land surface model (LSM), in association with precipitation, temperature records, and standard drought indicators. The analytical approach incorporates the development of 100+ yearlong TWSA records using a probabilistic conditional distribution fitting approach by the GAMLSS (generalized additive model for location, scale, and shape) model. The model performance was tested using standard indicators including coevolution plots, the Nash–Sutcliffe coefficient, cumulative density function, standardized residuals, and uncertainty bounds. All model evaluation results are satisfactory to excellent. The drought and flooding severity/magnitude, duration, and recurrence frequencies were assessed during the studied period. The results showed, (1) The NRB between 2002 to 2020 has witnessed a substantial transition to wetter conditions. Specifically, during the wet season, the NRB received between ~50 Gt./yr. to ~300 Gt./yr. compared to ~30 Gt./yr. to ~70 Gt./yr. of water loss during the dry season. (2) The TWSA reanalysis records between 1901 to 2002 revealed that the NRB had experienced a positive increase in TWS of ~17% during the wet season. Moreover, the TWS storage had witnessed a recovery of ~28% during the dry season. (3) The projected TWSA between 2021 to 2050 unveiled a positive increase in the TWS during the rainy season. While during the dry season, the water storage showed insubstantial TWS changes. Despite these projections, the future storage suggested a reduction between 10 to 30% in TWS. The analysis of drought and flooding frequencies between 1901 to 2050 revealed that the NRB has ~64 dry-years compared to ~86 wet-years. The exceedance probabilities for the normal conditions are between 44 to 52%, relative to a 4% chance of extreme events. The recurrence interval of the normal to moderate wet or dry conditions is ~6 years. These TWSA trajectories call for further water resources planning in the region, especially during flood seasons. This research contributes to the ongoing efforts to improve the TWSA assessment and its associated dynamics for transboundary river basins.

scholarly journals Bias correction of a gauge-based gridded product to improve extreme precipitation analysis in the Yarlung Tsangpo–Brahmaputra River basin

2020 ◽  
Vol 20 (8) ◽  
pp. 2243-2254
Author(s):  
Xian Luo ◽  
Xuemei Fan ◽  
Yungang Li ◽  
Xuan Ji
Keyword(s):  
River Basin ◽  
Bias Correction ◽  
Extreme Precipitation ◽  
Precipitation Data ◽  
Brahmaputra River ◽  
Extreme Precipitation Indices ◽  
Precipitation Analysis ◽  
Precipitation Indices ◽  
Yarlung Tsangpo ◽  
Local Intensity

Abstract. Critical gaps in the amount, quality, consistency, availability, and spatial distribution of rainfall data limit extreme precipitation analysis, and the application of gridded precipitation data is challenging because of their considerable biases. This study corrected Asian Precipitation Highly Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE) estimates in the Yarlung Tsangpo–Brahmaputra River basin (YBRB) using two linear and two nonlinear methods, and their influence on extreme precipitation indices was assessed by cross-validation. Bias correction greatly improved the performance of extreme precipitation analysis. The ability of four methods to correct wet-day frequency and coefficient of variation were substantially different, leading to considerable differences in extreme precipitation indices. Local intensity scaling (LOCI) and quantile–quantile mapping (QM) performed better than linear scaling (LS) and power transformation (PT). This study would provide a reference for using gridded precipitation data in extreme precipitation analysis and selecting a bias-corrected method for rainfall products in data-sparse regions.

scholarly journals Intensive Livestock Production Causing Antibiotic Pollution in the Yinma River of Northeast China

Water ◽  
2019 ◽  
Vol 11 (10) ◽  
pp. 2006
Author(s):  
Hanyu Ju ◽  
Sijia Li ◽  
Y. Xu ◽  
Guangxin Zhang ◽  
Jiquan Zhang
Keyword(s):  
Health Risk ◽  
River Basin ◽  
Rural Areas ◽  
Northeast China ◽  
Rural China ◽  
Livestock Production ◽  
Dry Season ◽  
Sample Collection ◽  
Ambient Conditions ◽  
Wet Season

Antibiotics are increasingly used in livestock production in rural China, raising concerns over pollution and health risk in countryside waterways. The Yinma River Basin in China’s far northeast is an agriculture-dominated area mixed with a densely populated province capitol city, providing a suitable area for investigating the influence of a typical land use mix in Northeast China on riverine antibiotic levels and transport. In this study, we sampled water along the Yinma River from upstream to downstream in a wet and a dry season and analyzed the samples for two popularly used antibiotics, ciprofloxacin (CIP) and norfloxacin (NOR). The goal of the study was to determine the spatiotemporal distribution of the antibiotics in Yinma’s two tributaries, Yitong and Yinma, which drain intensive livestock production land, and to elucidate which environmental and social factors influence the distribution of antibiotics in the cold and low mountainous areas. Water sample collection and instream measurements on dissolved oxygen and other ambient conditions were conducted at 17 locations along the Yinma and Yitong tributaries in August 2015 (wet season) and November 2015 (dry season). In addition to determining CIP and NOR levels, water samples were also analyzed for dissolved organic carbon (DOC), ammonia (NH3), and free chlorine. We found a significantly higher level of NOR when compared to CIP, indicating greater use of the first in livestock production. The level of both antibiotics was higher in the wet season (NOR: 61.063 ± 13.856 ng L−1; CIP: 3.453 ± 0.979 ng L−1) than in the dry season (57.435 ± 14.841 ng L−1; 3.091 ± 0.824 ng L−1), suggesting higher runoff of the antibiotics from the drainage area during the raining season. The level of antibiotics was higher in rural areas, especially forested and wetland areas where livestock typically graze, as well as in the lower river basin. However, the health risk of antibiotics is determined by the physical condition and lifestyle of the residents in the river basin, hence showing a higher vulnerability of the urban area than the rural area.

IMPACTS OF CLIMATE CHANGE ON IRRIGATED AGRICULTURE IN THE CHALIYAR RIVER BASIN IN KERALA, INDIA

2019 ◽  
Vol 01 (01) ◽  
pp. 1950001
Author(s):  
ARYA SOMAN ◽  
N. R. CHITHRA
Keyword(s):  
Climate Change ◽  
River Basin ◽  
Climate Model ◽  
Regional Climate ◽  
Dry Season ◽  
Water Requirement ◽  
Irrigated Agriculture ◽  
Max Planck ◽  
Wet Season ◽  
Impacts Of Climate Change

Impact assessment of regional climate change is very important as change in climate has emerged as one of the major threats to water resource systems and would significantly affect streamflow, soil moisture and water availability. The study used output of the Regional Climate Model (RCM) Remo2009 (Max-Planck-Institute (MPI)) to analyze the potential impacts of climate change on irrigated agriculture in the Chaliyar river basin, India. Streamflow and evapotranspiration were simulated using validated Hydrologic Engineering Center’s Hydrologic Modeling System (HEC-HMS) model. The estimation of irrigation water requirement (IWR) was performed using Food and Agriculture Organization (FAO) method for the period 2021–2030 and 2051–2060. Results show that projected streamflow increases during June to September and decreases during October to December and January to May in future. Crop water requirement and IWR showed an increase during dry season and decrease during wet season. The increase/decrease in streamflow and IWR during wet/dry season is more in the far future than near future and for RCP 8.5 scenario than RCP 4.5 scenario.

scholarly journals Seasonal dynamics of carbon and nutrients from two contrasting tropical floodplain systems in the Zambezi River basin

2015 ◽  
Vol 12 (24) ◽  
pp. 7535-7547 ◽  
Author(s):  
A. L. Zuijdgeest ◽  
R. Zurbrügg ◽  
N. Blank ◽  
R. Fulcri ◽  
D. B. Senn ◽  
...  
Keyword(s):  
Organic Matter ◽  
Primary Production ◽  
River Basin ◽  
Particulate Organic Matter ◽  
Dry Season ◽  
Nutrient Concentrations ◽  
Wet Season ◽  
Carbon And Nitrogen ◽  
Zambezi River Basin ◽  
Zambezi River

Abstract. Floodplains are important biogeochemical reactors during fluvial transport of carbon and nutrient species towards the oceans. In the tropics and subtropics, pronounced rainfall seasonality results in highly dynamic floodplain biogeochemistry. The massive construction of dams, however, has significantly altered the hydrography and chemical characteristics of many (sub)tropical rivers. In this study, we compare organic-matter and nutrient biogeochemistry of two large, contrasting floodplains in the Zambezi River basin in southern Africa: the Barotse Plains and the Kafue Flats. Both systems are of comparable size but differ in anthropogenic influence: while the Barotse Plains are still in large parts pristine, the Kafue Flats are bordered by two hydropower dams. The two systems exhibit different flooding dynamics, with a larger contribution of floodplain-derived water in the Kafue Flats and a stronger peak flow in the Barotse Plains. Distinct seasonal differences have been observed in carbon and nutrient concentrations, loads, and export and retention behavior in both systems. The simultaneous retention of particulate carbon and nitrogen and the net export of dissolved organic and inorganic carbon and nitrogen suggested that degradation of particulate organic matter was the dominant process influencing the river biogeochemistry during the wet season in the Barotse Plains and during the dry season in the Kafue Flats. Reverse trends during the dry season indicated that primary production was important in the Barotse Plains, whereas the Kafue Flats seemed to have both primary production and respiration occurring during the wet season, potentially occurring spatially separated in the main channel and on the floodplain. Carbon-to-nitrogen ratios of particulate organic matter showed that soil-derived material was dominant year-round in the Barotse Plains, whereas the Kafue Flats transported particulate organic matter that had been produced in the upstream reservoir during the wet season. Stable carbon isotopes suggested that inputs from the inundated floodplain to the particulate organic-matter pool were important during the wet season, whereas permanent vegetation contributed to the material transported during the dry season. This study revealed effects of dam construction on organic-matter and nutrient dynamics on the downstream floodplain that only become visible after longer periods, and it highlights how floodplains act as large biogeochemical reactors that can behave distinctly differently from the entire catchment.

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