Spatiotemporal Variations of Precipitation in China Using Surface Gauge Observations from 1961 to 2016

Global climate change is significant, and the spatiotemporal variations of precipitation associated with it are pronounced. Based on the daily precipitation data from 10 weather stations located from southeast to northwest across China from 1961–2017, the Mann–Kendall trend test was generally applied to analyze the spatiotemporal variations of precipitation. The factors influencing the precipitation changes were investigated. The results revealed that (1) the annual, summer, and winter rainfall amount (RA) exhibited increasing rates of 16.36, 12.31, and 2.49 mm/10 year, respectively. The change rates of annual rainfall days (RD) were 2.68 day/10 year in the northwest region and −1.88 day/10 year in the southeast. The annual and seasonal daily precipitation on rainy days (RP) exhibited an increasing trend. (2) All of the RA, RD, and RP values initially increased, then decreased, and then slightly increased from Southeast to Northwest China. These results proved that the RA increased with the increase of light rain in Northwest China and heavy rain in Southeast China. In addition, changes in the monsoon have altered the rate at which RA, RD, and RP vary with distance from the sea. These findings may help to provide suggestions for the rational spatial utilization of water resources in China.

Section: Comparison Of These Research Results With Those Of Other Stumentioning

confidence: 98%

Temporal and Spatial Variations of Precipitation Change from Southeast to Northwest China during the Period 1961–2017

Zhu

Liu

2020

“…It is an essential input for hydrological modelling and also forms the basis of hydrological, agricultural research applications, environment studies, and climate change studies [1,2]. It is seen that areas of high rain gauge density give more reliable precipitation estimates than those of low-density areas [3,4]. However, due to economic constraints and infeasible natural conditions, such as in the Arctic [5] and in the Tibetan Plateau, ground-based observations are usually sparse, especially in several developing countries, where ground-based rainfall observation networks have always been relatively sparse [6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Satellite Precipitation Products in Simulating Streamflow in a Humid Tropical Catchment of India Using a Semi-Distributed Hydrological Model

Sharannya

Al‐Ansari

Barma

et al. 2020

Precipitation obtained from rain gauges is an essential input for hydrological modelling. It is often sparse in highly topographically varying terrain, exhibiting a certain amount of uncertainty in hydrological modelling. Hence, satellite rainfall estimates have been used as an alternative or as a supplement to station observations. In this study, an attempt was made to evaluate the Tropical Rainfall Measuring Mission (TRMM) and Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS), employing a semi-distributed hydrological model, i.e., Soil and Water Assessment Tool (SWAT), for simulating streamflow and validating them against the flows generated by the India Meteorological Department (IMD) rainfall dataset in the Gurupura river catchment of India. Distinct testing scenarios for simulating streamflow were made to check the suitability of these satellite precipitation data. The TRMM was able to better estimate rainfall than CHIRPS after performing categorical and continuous statistical results with respect to IMD rainfall data. While comparing the performance of model simulations, the IMD rainfall-driven streamflow emerged as the best followed by the TRMM, CHIRPS-0.05, and CHIRPS-0.25. The coefficient of determination (R2), Nash–Sutcliffe efficiency (NSE), and percent bias (PBIAS) were in the range 0.63 to 0.86, 0.62 to 0.86, and −14.98 to 0.87, respectively. Further, an attempt was made to examine the spatial distribution of key hydrological signature, i.e., flow duration curve (FDC) in the 30–95 percentile range of non-exceedance probability. It was observed that TRMM underestimated the flow for agricultural water availability corresponding to 30 percent, even though it showed a good performance compared to the other satellite rainfall-driven model outputs.

“…Precipitation is a fundamental part of the hydrological cycle, which is of great significance in meteorology, hydrology, ecology and other scientific research areas [1,2]. Precipitation is a complex natural phenomenon which is characterized by a significant variability both in time and space [3][4][5]. Therefore, accurate precipitation data are extremely important for water resource-related research.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of TMPA Satellite Precipitation in Driving VIC Hydrological Model over the Upper Yangtze River Basin

Zhu

Huang

Zhang

et al. 2020

Although the Tropical Rainfall Measurement Mission (TRMM) has come to an end, the evaluation of TRMM satellite precipitation is still of great significance for the improvement of the Global Precipitation Measurement (GPM). In this paper, the hydrological utility of TRMM Multi-satellite Precipitation Analysis (TMPA) 3B42 RTV7/V7 precipitation products was evaluated using the variable infiltration capacity (VIC) hydrological model in the upper Yangtze River basin. The main results show that (1) TMPA 3B42V7 had a reliable performance in precipitation estimation compared with the gauged precipitation on both spatial and temporal scales over the upper Yangtze River basin. Although TMPA 3B42V7 slightly underestimated precipitation, TMPA 3B42RTV7 significantly overestimated precipitation at daily and monthly time scales; (2) the simulated runoff by the VIC hydrological model showed a high correlation with the gauged runoff and lower bias at daily and monthly time scales. The Nash–Sutcliffe coefficient of efficiency (NSCE) value was as high as 0.85, the relative bias (RB) was −6.36% and the correlation coefficient (CC) was 0.93 at the daily scale; (3) the accuracy of the 3B42RTV7-driven runoff simulation had been greatly improved by using the hydrological calibration parameters obtained from 3B42RTV7 compared with that of gauged precipitation. A lower RB (14.38% vs. 66.58%) and a higher CC (0.87 vs. 0.85) and NSCE (0.71 vs. −0.92) can be found at daily time scales when we use satellite data instead of gauged precipitation data to calibrate the VIC model. However, the performance of the 3B42V7-driven runoff simulation did not improve in the same operation accordingly. The cause might be that the 3B42V7 satellite products have been adjusted by gauged precipitation. This study suggests that it might be better to calibrate the parameters using satellite data in hydrological simulations, especially for unadjusted satellite data. This study is not only helpful for understanding the assessment of multi-satellite precipitation products in large-scale and complex areas in the upper reaches of the Yangtze River, but also can provide a reference for the hydrological utility of the satellite precipitation products in other river basins of the world.