Hydrological long-term dry and wet periods in the Xijiang River basin, South China

Estimation of total discharge is necessary to understand the hydrological cycle and to manage water resources efficiently. However, the task is problematic in an area where ground observations are limited. The North Korea region is one example. Here, the total discharge was estimated based on the water balance using multiple satellite products. They are the terrestrial water storage changes (TWSC) derived from the Gravity Recovery and Climate Experiment (GRACE), precipitation from the Tropical Rainfall Measuring Mission (TRMM), and evapotranspiration from the Moderate Resolution Imaging Spectroradiometer (MODIS). The satellite-based discharge was compared with land surface model products of the Global Land Data Assimilation System (GLDAS), and a positive relationship between the results was obtained (r = 0.70-0.86; bias = −9.08-16.99 mm/month; RMSE = 36.90-62.56 mm/month; NSE = 0.01-0.62). Among the four land surface models of GLDAS (CLM, Mosaic, Noah, and VIC), CLM corresponded best with the satellite-based discharge, satellite-based discharge has a tendency to slightly overestimate compared to model-based discharge (CLM, Mosaic, Noah, and VIC) in the dry season. Also, the total discharge data based on the Precipitation-Runoff Modeling System (PRMS) and the in situ discharge for major five river basins in South Korea show comparable seasonality and high correlation with the satellite-based discharge. In spite of the relatively low spatial resolution of GRACE, and loss of information incurred during the process of integrating three different satellite products, the proposed methodology can be a practical tool to estimate the total discharge with reasonable accuracy, especially in a region with scarce hydrologic data.

Section: Estimation Of Satellite-based Discharge In the Korean Peninsulasupporting

confidence: 51%

Total Discharge Estimation in the Korean Peninsula Using Multi-Satellite Products

Seo

2017

“…In this study, we also apply the widely-used SPI to characterize drought on different time scales [36]. The SPI is calculated from monthly precipitation data only [37], whereas other drought indices such as the Standardized Precipitation Evapotranspiration Index (SPEI; [38]) and the Palmer Drought Severity Index (PDSI; [39]) use several parameters.…”

Section: Descriptive Statistics Equation Unitmentioning

confidence: 99%

Evaluation of TRMM 3B43 Precipitation Data for Drought Monitoring in Jiangsu Province, China

Tao

Fischer

Zeng

et al. 2016

Satellite-based precipitation monitoring at high spatial resolution is crucial for assessing the water and energy cycles at the global and regional scale. Based on the recently released 7th version of the Multi-satellite Precipitation Analysis (TMPA) product of the Tropical Rainfall Measuring Mission (TRMM), and the monthly precipitation data (3B43) are evaluated using observed monthly precipitation from 65 meteorological stations in Jiangsu Province, China, for the period 1998-2014. Additionally, the standardized precipitation index (SPI), which is derived by a nonparametric approach, is employed to investigate the suitability of the TRMM 3B43 precipitation data for drought monitoring in Jiangsu Province. The temporal correlations between observations and the TRMM 3B43 precipitation data show, in general, reasonable agreement for different time scales. However, in summer, only 50% of the stations present correlation coefficients that are statistically significant at the 95% confidence interval. The overall best agreement of TRMM 3B43 precipitation data at seasonal scale tends to occur in autumn (SON). The comparative analysis of the calculated SPI time series suggests that the accuracy of TRMM3B43 decreases with increasing time scale. Stations with significant correlation coefficients also become less spatially homogeneous with increasing time scale. In summary, the findings demonstrate that TRMM 3B43 precipitation data can be used for reliable short-term drought monitoring in Jiangsu province, while temporal-spatial limitations exist for longer time scales.

“…The two rainfall-runoff models successfully simulate the historical runoff and show similar simulation results for the eight catchments in the YTR basins. Fischer et al (2013) [16] used the standardized precipitation index (SPI-24) and the standardized discharge index (SDI-24) were used to analysis the hydrological long-term dry and wet sequences on the Xijiang River without using any hydrological models.…”

Section: Introductionmentioning

confidence: 99%

Hydrological Appraisal of Climate Change Impacts on the Water Resources of the Xijiang Basin, South China

Zhu

Das

2017

Assessing the impact of climate change on streamflow is critical to understanding the changes to water resources and to improve water resource management. The use of hydrological models is a common practice to quantify and assess water resources in such situations. In this study, two hydrological models with different structures, e.g., a physically-based distributed model Liuxihe (LXH) and a lumped conceptual model Xinanjiang (XAJ) are employed to simulate the daily runoff in the Xijiang basin in South China, under historical (1964China, under historical ( -2013 and future climate conditions. The future climate series are downscaled from a global climate model (Beijing Climate Centre-Climate System Model, BCC-CSM version 1.1) by a high-resolution regional climate model under two representative concentration pathways-RCP4.5 and RCP8.5. The hydrological responses to climate change via the two rainfall-runoff models with different mathematical structures are compared, in relation to the uncertainties in hydrology and meteorology. It is found that the two rainfall-runoff models successfully simulate the historical runoff for the Xijiang basin, with a daily runoff Nash-Sutcliffe Efficiency of 0.80 for the LXH model and 0.89 for the XAJ model. The characteristics of high flow in the future are also analysed including their frequency (magnitude-return-period relationship). It shows that the distributed model could produce more streamflow and peak flow than the lumped model under the climate change scenarios. However the difference of the impact from the two climate scenarios is marginal on median monthly streamflow. The flood frequency analysis under climate change suggests that flood magnitudes in the future will be more severe than the historical floods with the same return period. Overall, the study reveals how uncertain it can be to quantify water resources with two different but well calibrated hydrological models.