This study investigated the annual and seasonal rainfall variability at five selected stations of Amhara Regional State, by using the innovative trend analysis method (ITAM), Mann-Kendall (MK) and Sen’s slope estimator test. The result showed that the trend of annual rainfall was increasing in Gondar (Z = 1.69), Motta (Z = 0.93), and Bahir Dar (Z = 0.07) stations. However, the trends in Dangla (Z = −0.37) and Adet (Z = −0.32) stations showed a decreasing trend. As far as monthly and seasonal variability of rainfall are concerned, all the stations exhibited sensitivity of change. The trend of rainfall in May, June, July, August, and September was increasing. However, the trend on the rest of other months showed a decreasing trend. The increase in rainfall during Kiremt season, along with the decrease in number of rainy days, leads to an increase of extreme rainfall events over the region during 1980–2016. The consistency in rainfall trends over the study region confirms the robustness of the change in trends. Innovative trend analysis method is very crucial method for detecting the trends in rainfall time series data due to its potential to present the results in graphical format as well. The findings of this paper could help researchers to understand the annual and seasonal variability of rainfall over the study region and become a foundation for further studies.

Machine learning algorithms are becoming more and more popular in natural disaster assessment. Although the technology has been tested in flood susceptibility analysis of several watersheds, research on global flood disaster risk assessment based on machine learning methods is still rare. Considering that the watershed is the basic unit of water management, the purpose of this study was to conduct a risk assessment of floods in the global fourth-level watersheds. Thirteen conditioning factors were selected, including: maximum daily precipitation, precipitation concentration degree, altitude, slope, relief degree of land surface, soil type, Manning coefficient, proportion of forest and shrubland, proportion of artificial surface, proportion of cropland, drainage density, population, and gross domestic product. Four machine learning algorithms were selected in this study: logistic regression, naive Bayes, AdaBoost, and random forest. The global susceptibility assessment model was constructed based on four machine learning algorithms, thirteen conditioning factors, and global flood inventories. The evaluation results of the model show that the random forest performed better in the test, and is an efficient and reliable tool in flood susceptibility assessment. Sensitivity analysis of the conditioning factors showed that precipitation concentration degree and Manning coefficient were the main factors affecting flood risk in the watersheds. The susceptibility map showed that fourth-level watersheds in the global high-risk area accounted for a large proportion of the total watersheds. With the increase of extreme hydrological events caused by climate change, global flood disasters are still one of the most threatening natural disasters. The global flood susceptibility map from this study can provide a reference for global flood management.

Under the influence of global climate change, the discharges of major global rivers directed into the ocean have undergone significant changes. To study the trends and causes in discharge variation, we selected 40 large rivers and analyzed their annual discharges near their estuaries from 1960 to 2010. The method of runoff variation attribution analysis based on the Budyko hypothesis for large-scale basins was developed, in which influencing factors of human activities and glacial melting factors were added to the formula. The contribution rate of climate factors and human activities to changes in discharge were quantitatively identified. Climatic factors include precipitation, evapotranspiration and glacial melting. Human activity factors include underlying surface and artificial water transfer. The contribution rate is determined by the elastic coefficient, which is obtained by the ratio of change rate of each factor and the change rate of runoff. The results indicated that the discharges predominantly showed downward trends with a few upward trends. Rivers in North America and Africa showed downward trends, and those in Europe principally showed upward trends. Climate was the main influencing factor of discharges changes, and only approximately 25% of river discharges were greatly affected by human activities. River discharges in 75% of the basins which mainly contains subtropical monsoon humid climate and savanna climate zones showed upward trends. In the four basins which are mainly contains tropical rainforest climate and tropical monsoon climate, they all showed downward trends. The trend of discharges in the temperate monsoon climate, temperate continental climate, and temperate maritime climate cannot be accurately judged because of irregular variation. The discharges in the mid-high latitudinal zones predominantly showed upward trends, while those in the mid-low latitudinal zones with the influence of human activities showed downward trends.

The Awash river basin has been the most extensively developed and used river basin in Ethiopia since modern agriculture was introduced. This paper investigated the annual precipitation, temperature, and river discharge variability using the innovative trend analysis method (ITAM), Mann–Kendall (MK) test, and Sen’s slope estimator test. The results showed that the trend of annual precipitation was significantly increasing in Fitche (Z = 0.82) and Gewane (Z = 0.80), whereas the trend in Bui (Z = 69) was slightly decreasing and the trend in Sekoru (Z = 0.45) was sharply decreasing. As far as temperature trends were concerned, a statistically significant increasing trend was observed in Fitche (Z = 3.77), Bui (Z = 4.84), and Gewane (Z = 5.59). However, the trend in Sekoru (Z = 1.37) was decreasing with statistical significance. The discharge in the study basin showed a decreasing trend during the study period. Generally, the increasing and decreasing levels of precipitation, temperature, and discharge across the stations in this study indicate the change in trends. The results of this study could help researchers, policymakers, and water resources managers to understand the variability of precipitation, temperature, and river discharge over the study basin.

Quality and representative precipitation data play an essential role in hydro-meteorological analyses. However, the required reliability and coverage is often unavailable from conventional gauge observations. As a result, globally available precipitation datasets are being used as an alternative or supplementary to gauge observations. In this study, the accuracy of three recently released, high-resolution precipitation datasets with a spatial resolution of 0.1° and a daily temporal resolution is evaluated over the Upper Blue Nile River Basin (UBNRB) for the period of 2007 to 2016. The datasets are Integrated Multi-satellitE Retrievals for GPM version 6 (IMERG6), Multi-Source Weighted-Ensemble Precipitation version 2.2 (MSWEP2.2) and soil moisture to rain using Advanced SCATterometer version 1.1 (SM2RAIN-ASCAT1.1). The comparison was made between rain gauge observations and two other high-resolution precipitation datasets named Enhancing National Climate Services (ENACTS) and Climate Hazards Group Infrared Precipitation with Stations version 2 (CHIRPS2). The modified Kling-Gupta efficiency (KGE’) and four categorical indices named probability of detection (POD), false alarm ratio (FAR), critical success index (CSI), and frequency bias (fBIAS) was used to measure the skills of each dataset. Results revealed that, except SM2RAIN-ASCAT1.1, all other datasets show a better ability on a monthly time scale for areas with an elevation below 1500 m above sea level (m.a.s.l). The overall performance was better in the wetter months of March to August than the drier months of September to February. Besides, all products including SM2RAIN-ASCAT1.1 could detect no rain events (rain < 1 mm) correctly, but their skill deteriorates on identifying higher intensity events. By comparison, ENACTS (calibrated with most quality gauges of Ethiopia) and CHIRPS2 exhibited the best performance due to their high-resolution nature and inclusion of physiographic information in their data generation procedures. IMERG6 and MSWEP2.2 showed the next best performance according to both the continuous and categorical indices used. SM2RAIN-ASCAT1.1 demonstrates the least skill everywhere due to problems that could be associated with misinterpretations of soil moisture signals by the SM2RAIN algorithm. Considering the scarcity of gauged datasets over UBNRB, IMERG6 and MSWEP2.2 could be regarded as valuable datasets for hydro-climatic analysis, mainly where gauge density is low. SM2RAIN-ASCAT1.1, on the other hand, needs significant bias correction to treat its apparent wet biases before any application.

The Huaihe River Basin is located in-between the north-south climate transitional zone in China and is China’s important congested area and production base. The land-use of the environment was occupied by the land-use for social and economics. This paper aims to have a comprehensive understanding about land-use and vegetation evolution of the basin over the past 30 years. In view of 5 years (1985, 1990, 2000, 2005, and 2014) land-use data and remote sensing data about NDVI, land-use dynamic degree, and land-use transfer matrix were used to analyze the dynamics of land use. Spatial overlay was used to study vegetation change characteristics of the basin. The present study investigates the evolution trend of vegetation coverage based on spatial overlay analysis. We found that water bodies and urban lands of the basin increased during 1985-2014. On the other hand, the area of artificial vegetation, natural vegetation, and wetland were reduced. The impact was gradually increased by human intervention on various land use types. Overall vegetation coverage level shows deteriorative development, and distribution areas were discrete. The excellent vegetation coverage of natural vegetation and wetland didn’t have obvious changes, but the high coverage significantly decreased. Taken as a whole, the natural vegetation coverage was reduced. The vegetation coverage level of artificial water in the land-use for social and economics had greatly increased, but the overall trend of vegetation coverage level of artificial vegetation and resident construction land showed a decreasing trend.