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The natural runoff of the Yellow River has been reduced sharply since 2000. Based on rainfall network of 1966 and 1977 are built on GIS platform, the trend of rainfall variety in 1956∼2013 is analyzed. The turning point of runoff of natural period and human activities impact period in the eighteen tributaries was analyzed with the method of orderly clustering and MWP test and so on. The model was established with the data from 1956 to the turning point year, and the runoff during this period was regarded as benchmark value. The runoff variety and the impacts brought by rainfall in the status series from 2007 to 2013 were analyzed by equation of runoff division. It is showed that natural runoff reduced 5.411 billion cubic meters, among which brought by rainfall not negative value but increased 0.424 billion cubic meter.

This paper studied 5 runoff plots of rainfall, runoff and soil erosion in Zhejiang province, China. The results showed that: (1) Annual rainfall was mainly concentrated in 4-9 months, accounting for 61%-67% of annual rainfall.(2) Annual runoff was mainly concentrated in 5-9 months, accounting for 67%-76% of annual runoff.(3) The amount of soil erosion was mainly concentrated in 5-9 months, accounting for 68%-87% of annual runoff. (4) Soil erosion was closely related to rainfall and runoff.

Climate change has led to concerns about increasing river floods resulting from the greater water-holding capacity of a warmer atmosphere. These concerns are reinforced by evidence of increasing economic losses associated with flooding in many parts of the world, including Europe. Any changes in river floods would have lasting implications for the design of flood protection measures and flood risk zoning. However, existing studies have been unable to identify a consistent continental-scale climatic-change signal in flood discharge observations in Europe, because of the limited spatial coverage and number of hydrometric stations. Here we demonstrate clear regional patterns of both increases and decreases in observed river flood discharges in the past five decades in Europe, which are manifestations of a changing climate. Our results-arising from the most complete database of European flooding so far-suggest that: increasing autumn and winter rainfall has resulted in increasing floods in northwestern Europe; decreasing precipitation and increasing evaporation have led to decreasing floods in medium and large catchments in southern Europe; and decreasing snow cover and snowmelt, resulting from warmer temperatures, have led to decreasing floods in eastern Europe. Regional flood discharge trends in Europe range from an increase of about 11 per cent per decade to a decrease of 23 per cent. Notwithstanding the spatial and temporal heterogeneity of the observational record, the flood changes identified here are broadly consistent with climate model projections for the next century, suggesting that climate-driven changes are already happening and supporting calls for the consideration of climate change in flood risk management.

A large proportion of dryland trees and shrubs (hereafter referred to collectively as trees) grow in isolation, without canopy closure. These non-forest trees have a crucial role in biodiversity, and provide ecosystem services such as carbon storage, food resources and shelter for humans and animals 1 , 2 . However, most public interest relating to trees is devoted to forests, and trees outside of forests are not well-documented 3 . Here we map the crown size of each tree more than 3 m 2 in size over a land area that spans 1.3 million km 2 in the West African Sahara, Sahel and sub-humid zone, using submetre-resolution satellite imagery and deep learning 4 . We detected over 1.8 billion individual trees (13.4 trees per hectare), with a median crown size of 12 m 2 , along a rainfall gradient from 0 to 1,000 mm per year. The canopy cover increases from 0.1% (0.7 trees per hectare) in hyper-arid areas, through 1.6% (9.9 trees per hectare) in arid and 5.6% (30.1 trees per hectare) in semi-arid zones, to 13.3% (47 trees per hectare) in sub-humid areas. Although the overall canopy cover is low, the relatively high density of isolated trees challenges prevailing narratives about dryland desertification 5 – 7 , and even the desert shows a surprisingly high tree density. Our assessment suggests a way to monitor trees outside of forests globally, and to explore their role in mitigating degradation, climate change and poverty. Deep learning was used to map the crown sizes of each tree in the West African Sahara, Sahel and sub-humid zone using submetre-resolution satellite imagery, revealing a relatively high density of trees in arid areas.

Over-exploitation of groundwater and marked changes in climate over the years have imposed immense pressure on the global groundwater resources. As demand of potable water increases across the globe for human consumption, agriculture and industrial uses, the need to evaluate the groundwater potential and productivity of aquifers also increases. In the recent years, geographic information system based studies have gained much prominence in groundwater exploration because it is rapid and will provide first - hand information on the resource for further developments. Therefore, the present study has been undertaken with an objective to delineate the groundwater potential of a small tropical river basin located in the western side of the Western Ghats in India as an example. A combination of geographical information system and analytical hierarchical process techniques (AHP) was used in the present study. A total of 12 thematic layers such as Geology, Geomorphology, Land Use/Land Cover, Lineament density, Drainage density, Rainfall, Soil, Slope, Roughness, Topographic Wetness Index, Topographic Position Index and Curvature were prepared and studied for groundwater potential zone demarcation. Weights assigned to each class in all the thematic maps are based on their characteristics and water potential capacity through AHP method. The accuracy of the output was cross-validated with information on groundwater prospects of the area and the overall accuracy of the method comes to around 85%. The groundwater potential zone map thus obtained was categorized into five classes-very high, high, moderate, low and very low. The study reveals that about 59% of the river basin is covered under moderate groundwater potential zone. The low and high groundwater potential zones are observed in 29% and 11% respectively. Area under very high and very low potential zones are recorded only in very limited areas in the basin.

Iran is experiencing unprecedented climate-related problems such as drying of lakes and rivers, dust storms, record-breaking temperatures, droughts, and floods. Here, we use the ensemble of five high-resolution climate models to project maximum and minimum temperatures and rainfall distribution, calculate occurrences of extreme temperatures (temperatures above and below the historical 95th and 5th percentiles, respectively), analyze compound of precipitation and temperature extremes, and determine flooding frequencies across the country. We found that compared to the period of 1980–2004, in the period of 2025–2049, Iran is likely to experience more extended periods of extreme maximum temperatures in the southern part of the country, more extended periods of dry (for ≥120 days: precipitation <2 mm, Tmax ≥30 °C) as well as wet (for ≤3 days: total precipitation ≥110 mm) conditions, and higher frequency of floods. Overall, the combination of these results projects a climate of extended dry periods interrupted by intermittent heavy rainfalls, which is a recipe for increasing the chances of floods. Without thoughtful adaptability measures, some parts of the country may face limited habitability in the future.

This study analyzes and forecasts the long-term Spatio-temporal changes in rainfall using the data from 1901 to 2015 across India at meteorological divisional level. The Pettitt test was employed to detect the abrupt change point in time frame, while the Mann-Kendall (MK) test and Sen’s Innovative trend analysis were performed to analyze the rainfall trend. The Artificial Neural Network-Multilayer Perceptron (ANN-MLP) was employed to forecast the upcoming 15 years rainfall across India. We mapped the rainfall trend pattern for whole country by using the geo-statistical technique like Kriging in ArcGIS environment. Results show that the most of the meteorological divisions exhibited significant negative trend of rainfall in annual and seasonal scales, except seven divisions during. Out of 17 divisions, 11 divisions recorded noteworthy rainfall declining trend for the monsoon season at 0.05% significance level, while the insignificant negative trend of rainfall was detected for the winter and pre-monsoon seasons. Furthermore, the significant negative trend (−8.5) was recorded for overall annual rainfall. Based on the findings of change detection, the most probable year of change detection was occurred primarily after 1960 for most of the meteorological stations. The increasing rainfall trend had observed during the period 1901–1950, while a significant decline rainfall was detected after 1951. The rainfall forecast for upcoming 15 years for all the meteorological divisions’ also exhibit a significant decline in the rainfall. The results derived from ECMWF ERA5 reanalysis data exhibit that increasing/decreasing precipitation convective rate, elevated low cloud cover and inadequate vertically integrated moisture divergence might have influenced on change of rainfall in India. Findings of the study have some implications in water resources management considering the limited availability of water resources and increase in the future water demand.

Rainfall is a key source to diminish the problem of water scarcity in the arid and semi-arid regions. Rainwater harvesting is considered an imperious tool for rainwater conservation. Locating the appropriate location for rainwater harvesting structure plays an important role to increase water availability and improve water resources planning. The main goal of this paper is to recognize the proper location for a rainwater harvesting structure using a suitability model generated with ModelBuilder in ArcGIS. Six thematic layers i.e. soil structure, slope, drainage density, vegetation cover, distance to the roads, and runoff depth, are considered to find the proper site for rainwater harvesting structure. The result shows that 12% represents the suitable zone of the total study area, 42% represents the medium suitable area, and 46% represents not suitable areas to implement rainwater harvesting structure. The application of this scheme should maintain any policy adoption for site selection for rainwater harvesting.

As the Earth’s atmosphere warms, the atmospheric circulation changes. These changes vary by region and time of year, but there is evidence that anthropogenic warming causes a general weakening of summertime tropical circulation 1 – 8 . Because tropical cyclones are carried along within their ambient environmental wind, there is a plausible a priori expectation that the translation speed of tropical cyclones has slowed with warming. In addition to circulation changes, anthropogenic warming causes increases in atmospheric water-vapour capacity, which are generally expected to increase precipitation rates 9 . Rain rates near the centres of tropical cyclones are also expected to increase with increasing global temperatures 10 – 12 . The amount of tropical-cyclone-related rainfall that any given local area will experience is proportional to the rain rates and inversely proportional to the translation speeds of tropical cyclones. Here I show that tropical-cyclone translation speed has decreased globally by 10 per cent over the period 1949–2016, which is very likely to have compounded, and possibly dominated, any increases in local rainfall totals that may have occurred as a result of increased tropical-cyclone rain rates. The magnitude of the slowdown varies substantially by region and by latitude, but is generally consistent with expected changes in atmospheric circulation forced by anthropogenic emissions. Of particular importance is the slowdown of 21 per cent and 16 per cent over land areas affected by western North Pacific and North Atlantic tropical cyclones, respectively, and the slowdown of 22 per cent over land areas in the Australian region. The unprecedented rainfall totals associated with the ‘stall’ of Hurricane Harvey 13 – 15 over Texas in 2017 provide a notable example of the relationship between regional rainfall amounts and tropical-cyclone translation speed. Any systematic past or future change in the translation speed of tropical cyclones, particularly over land, is therefore highly relevant when considering potential changes in local rainfall totals. The translation speed of tropical cyclones has decreased globally by 10% over the past 70 years, compounding the increases in cyclone-related local rainfall that have resulted from anthropogenic warming.

Detecting changes in climate is a prerequisite for a better understanding of the climate and developing adaptation and mitigation measures at a regional and local scale. In this study long-term trends in rainfall and maximum and minimum temperature (T-max and T-min) were analysed on seasonal and annual time scales for East Africa. High resolution gridded rainfall (1981–2016) and temperature (1979–2010) data from international databases like the Climate Hazards Group are used. Long-term seasonal trend analysis shows a non-significant (except for small areas), decreasing (increasing) trend in rainfall in eastern (western) parts of Ethiopia and Kenya and a decreasing trend in large parts of Tanzania during the long rainy season. On the other hand, a non-significant increasing trend in large parts of the region is observed during the short rain season. With regard to annual trends, results largely confirm seasonal analyses: only a few significant trends in rainfall, but significant increasing trends in T-max (up to 1.9 °C) and T-min (up to 1.2 °C) for virtually the whole region. Our results demonstrate the need and added value of analysing climate trends based on data with high spatial resolution allowing sustainable adaptation measures at local scales.