Muhammad Imran Khan

Precipitation variability assessment of northeast China: Songhua River basin

Variability in precipitation is critical for the management of water resources. In this study, the research entropy base concept was applied to investigate spatial and temporal variability of the precipitation during 1964–2013 in the Songhua River basin of Heilongjiang Province in China. Sample entropy was applied on precipitation data on a monthly, seasonally, annually, decade scale and the number of rainy days for each selected station. Intensity entropy and apportionment entropy were used to calculate the variability over individual year and decade, respectively. Subsequently, Spearman’s Rho and Mann–Kendall tests were applied to observe for trends in the precipitation time series. The statistics of sample disorder index showed that the precipitation during February (mean 1.09, max. 1.26 and min. 0.80), April (mean 1.12, max. 1.29 and min. 0.99) and July (mean 1.10, max. 1.20 and min. 0.98) contributed significantly higher than those of other months. Overall, the contribution of the winter season was considerably high with a standard deviation of 0.10. The precipitation variability on decade basis was observed to increase from decade 1964–1973 and 1994–2003 with a mean value of decadal apportionment disorder index 0.023 and 0.053, respectively. In addition, the Mann–Kendall test value (1.90) showed a significant positive trend only at the Shangzhi station.

Rainfall Extremes: a Novel Modeling Approach for Regionalization

The rainfall events of extreme magnitude over the past few decades have caused destructive damages to lives and properties, especially in the subcontinent (e.g. Pakistan, India, Bangladesh etc). Rainfall hazard maps for these areas can be of great practical and theoretical interests. In our work, we used extreme value analysis and spatial interpolation techniques to provide such maps through a combination of the Tropical Rainfall Measuring Mission Precipitation (TRMM) 3B42 product and raingauge data. This mixed approach takes advantage of both the long time series available at a limited number of stations, and the large spatial coverage of the satellite data which, instead, has a poor temporal extent. The methodology is implemented by (1) creating a unique growth curve for the homogeneous region by utilizing in-situ rainfall data and (2) mapping the parameters of intensity-duration functions for the entire length of the study area by using TRMM 3B42 product. The regional results obtained by using mixed approach and TRMM 3B42 are compared with the estimates obtained by using in-situ data. The comparison showed that the overall output of mixed approach is more consistent with what transpired by in-situ data for a pre-defined return period.

Projection Pursuit Evaluation Model of Regional Surface Water Environment Based on Improved Chicken Swarm Optimization Algorithm

A Projection Pursuit Evaluation model of surface water environment based on an Improved Chicken Swarm Optimization Algorithm (ICSOA-PPE) is constructed using the ICSOA to optimize the optimal projection direction. Using the Jiansanjiang Administration in Heilongjiang Province, China as an example, 15 subordinate farms were used as an evaluation unit by selecting water quality indexes including CODMn, NH3-N, TP, TN, F− to evaluate the environmental quality of surface water using the ICSOA-PPE model. The results show that the environmental quality of surface water from all farms in this region was generally poor, except for that at the Qinglongshan, Qindeli and Daxing farms. These three farms met the standard for drinking water sources, while the remaining farms failed to reach the standard. By analyzing the relationship between the total amount of chemical fertilizer application per ha, the amount of nitrogen fertilizer application per ha, the amount of phosphate fertilizer application per ha and the environmental quality of the surface water, a conclusion could be reached that the total amount of chemical fertilizer has a substantial effect on water environment. Additionally, the contribution rate of the amount of nitrogen fertilizer application per ha to the organic pollution and the concentration of NH3-N is substantial, and the amount of phosphate fertilizer influences the water environmental quality to some extent. An analysis and comparison of the traversal capacity, the offset capacity and the convergence capacity of the Genetic Algorithm (GA), the Chicken Swarm Optimization Algorithm (CSOA) and ICSOA reveal that ICSOA is the better optimization algorithm, indicating that the ICSOA-PPE model is logical and reliable.

Multifractal Detrended Fluctuation Analysis of Regional Precipitation Sequences Based on the CEEMDAN-WPT

In this paper, the complete ensemble empirical mode decomposition with the adaptive noise (CEEMDAN) algorithm is introduced into the complexity research of precipitation systems to improve the traditional complexity measure method specific to the mode mixing of the Empirical Mode Decomposition (EMD) and incomplete decomposition of the ensemble empirical mode decomposition (EEMD). We combined the CEEMDAN with the wavelet packet transform (WPT) and multifractal detrended fluctuation analysis (MF-DFA) to create the CEEMDAN-WPT-MFDFA, and used it to measure the complexity of the monthly precipitation sequence of 12 sub-regions in Harbin, Heilongjiang Province, China. The results show that there are significant differences in the monthly precipitation complexity of each sub-region in Harbin. The complexity of the northwest area of Harbin is the lowest and its predictability is the best. The complexity and predictability of the middle and Midwest areas of Harbin are about average. The complexity of the southeast area of Harbin is higher than that of the northwest, middle, and Midwest areas of Harbin and its predictability is worse. The complexity of Shuangcheng is the highest and its predictability is the worst of all the studied sub-regions. We used terrain and human activity as factors to analyze the causes of the complexity of the local precipitation. The results showed that the correlations between the precipitation complexity and terrain are obvious, and the correlations between the precipitation complexity and human influence factors vary. The distribution of the precipitation complexity in this area may be generated by the superposition effect of human activities and natural factors such as terrain, general atmospheric circulation, land and sea location, and ocean currents. To evaluate the stability of the algorithm, the CEEMDAN-WPT-MFDFA was compared with the equal probability coarse graining LZC algorithm, fuzzy entropy, and wavelet entropy. The results show that the CEEMDAN-WPT-MFDFA was more stable than 3 contrast methods under the influence of white noise and colored noise, which proves that the CEEMDAN-WPT-MFDFA has a strong robustness under the influence of noise.

Assessment of characteristics and distinguished hydrological periods of a river regime

This study was carried out to analyze the hydrological characteristics and assess the distinguished hydrological periods of Upper Indus Basin (UIB) Rivers of Pakistan. For this purpose, statistical analysis (variation coefficient, the auto-correlation coefficient, sequential Mann–Kendall’s test) and a proposed method for distinguishing hydrological periods (described in methodology section) were applied. The results revealed that all rivers reflect moderate variability. The results of auto-correlation displayed that the river flow observed at Astore gauging station only indicated independency, while for Gilgit, Hunza and Kachura guaging at Indus River exhibited 2, 2, 4-year lag. The mutation analysis indicated that after 1980, the change point occurred at all UIB rivers. During analysis, it was also observed that river regimes have the same hydrological periods (i.e., 4), but with different dates of occurrence. The Gilgit River showed a low high-flow hydrological period compared to Astore, Hunza and Kachora (Indus). This difference may be due to the river’s own area natural conditions. The current analysis may be helpful for planning and management of water resources, designing of hydraulic structures and to make better policies in response to agricultural water requirement downstream of UIB River.

Complexity measurement of precipitation series in urban areas based on particle swarm optimized multiscale entropy

Entropy theory is commonly applied to study the complexity of hydrological systems. In view of the subjectivity and uncertainty of parameter selection in entropy theory research, this paper combines a particle swarm optimization (PSO) algorithm with entropy theory to improve the traditional parameter selection method and the accuracy and reliability of the complexity measurement results. We combined PSO and multiscale entropy (MSE) to analyze the complexity of monthly precipitation series from 11 stations in Harbin, Heilongjiang Province, China, and the complexity results were classified into three levels. Harbin, Shuangcheng, Yilan, and Fangzheng are level I areas; Wuchang, Tonghe, Yanshou, and Binxian are level II areas; and Bayan, Mulan, and Shangzhi are level III areas. We selected the mountainous area ratio, water area ratio, GDP, and grain production as indicators that influence the complexity of local precipitation. The results showed that the correlation between the precipitation complexity and terrain was obvious, the impact of the water area ratio on the precipitation complexity was small, and the GDP and grain production were mostly negatively correlated with precipitation. These results reveal the spatiotemporal characteristics of the precipitation complexity and the factors that potentially influence the complexity. This study provides a reference model for other complexity measures in the field of regional water resource systems and related studies.