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.

Soil nitrogen in farmland ecosystems is affected by climate, soil physical and chemical properties and planting activities. To clarify the effects of these factors on soil nitrogen in sloping farmland quantitatively, the distribution of soil total nitrogen (TN) content, nitrate nitrogen (NO₃-N) content and ammonium nitrogen (NH₄-N) content at depth of 0–100 cm on 11 profiles of the Luanhe River Basin were analyzed. Meanwhile, soil physical and chemical properties, climatic factors and NDVI (Normalized Difference Vegetation Index) were used to construct a structural equation which reflected the influence mechanism of environmental factors on soil nitrogen concentration. The results showed that TN and NO₃-N content decreased with the increase of soil depth in the Luanhe River Basin, while the variation of NH₄-N content with soil depth was not obvious. Soil organic carbon (SOC) content, soil pH, soil area average particle size (SMD) and NDVI6 (NDVI of June) explained variation of TN content by 77.4%. SOC was the most important environmental factor contributing to the variation of TN content. NDVI5 (NDVI of May), annual average precipitation (MAP), soil pH and SOC explained 49.1% variation of NO₃-N content. Among all environmental factors, only NDVI8 (NDVI of August) had significant correlation with soil NH₄-N content, which explained the change of NH₄-N content by 24.2%. The results showed that soil nitrogen content in the sloping farmland ecosystem was mainly affected by natural factors such as soil parent material and climate.

The foundation of managing excess nutrients in river is the identification of key physical processes and the control of decisive influencing factors. The existing studies seldom consider the influence of rainfall-runoff relationship and only focus on a few anthropogenic activities and natural attributes factors. To address this issue, a comprehensive set of influencing factors including rainfall-runoff relationship (represented by runoff coefficient), basic physical and chemical parameters of water quality, land use types, landscape patterns, topography, and socioeconomic development was constructed in this study. M–K test and cluster analysis were conducted to identify the temporal mutation and spatial clustering characteristics of NH₃-N and TP in Huangshui River basin, respectively. Partial least squares regression was used to elucidate the linkages between water contaminants and the factors. As shown in the results, the temporal mutations of NH₃-N and TP were obvious in the middle reaches, with 4 out of 7 catchments in the middle reaches have a larger number of mutations of NH₃-N than other catchments. The cluster analysis results of NH₃-N and TP among catchments were similar. This study also indicated that although the Huangshui River basin was located in the upper reaches of the Yellow River, the influences of rainfall-runoff relationship on spatiotemporal changes of NH₃-N and TP in its sub-basins were limited. Only the temporal change of NH₃-N in Jintan catchment in the upstream area was significantly affected by runoff coefficient. The indexes of proportion of water area (PWA), proportion of impervious area (PIA), and proportion of primary industry (PPI) were the top three influencing factors of temporal variation of NH₃-N and TP for most catchments in the middle reaches. The temporal change of NH₃-N in Jintan catchment in the upstream area was obviously affected by runoff coefficient. The spatial variation of NH₃-N and TP were all affected by PWA and proportion of secondary industry significantly. The results of this study can provide theoretical basis and technical support for the control and management of nitrogen and phosphorus pollution in upper reaches of rivers.

Climate change, topographical evolution and human activities are the main driving factors of NDVI spatiotemporal evolution. Quantitative identification of the driving mechanism can provide support for water conservation, artificial forest construction and soil erosion control. Taking Huangshui River Basin as an example, accumulated temperature, accumulated precipitation and NDVI of 16-days from 2000 to 2018 were collected and manipulated based on slope trend analysis, correlation analysis and other methods to identify the spatial and temporal distribution characteristics of NDVI in this study. The impact mechanisms of climate factors, topography and land use on spatial and temporal distribution characteristics of NDVI were quantitatively analyzed as well. The results show that: (1) the annual average growth rate of NDVI in Huangshui River Basin from 2000 to 2018 is 0.28%/a. NDVI in spring, summer and autumn also showed an increasing trend. The increasing area accounts for 38.04% of the whole basin, which was mainly distributed in the middle and lower reaches and northwest of Huangshui River Basin. (2) NDVI was positively correlated with accumulated temperature, accumulated precipitation and effective accumulated precipitation of 16 days, and the areas with (extreme) significant positive correlation accounted for 77.89%, 86.52% and 86.18% of the whole basin respectively. However, the correlation between NDVI and 16-days accumulated temperature increased first and then decreased from southeast to northwest in Huangshui River Basin. While the correlation between NDVI and accumulated precipitation (or effective accumulated precipitation) gradually increased from southeast to northwest. (3) NDVI is affected not only by altitude and aspect, but also by surface conditions and human factors. The areas with high NDVI are mainly distributed above 2700m above sea level. Compared with other slope aspect, NDVI on the semi-shady slope is higher, the growth rates of NDVI on semi-sunny slope and shady slope are the fastest, and the growth rate on sunny slope is the slowest. The growth of NDVI on shady slope in spring and autumn is the fastest, that on semi-sunny slope in summer is the fastest, and that on sunny slope in autumn, spring and summer is the slowest. (4) The annual average growth rate of NDVI in arable land, woodland, grassland, water area and unused land are 0.36%, 0.27%, 0.28%, 0.24% and 0.17% respectively. The annual average decrease rate of NDVI in urban land use is 0.13%. Large area increase of plantation forest is an important factor for increase of NDVI.

Slope ecological restoration and climate change are important factors affecting the hydrological processes of the Huangshui River Basin in Qinghai province, China. How to quantitatively identify the impact of slope ecological restoration on runoff and whether slope ecological restoration can mitigate the impact of future climate change on runoff are both very important. In this paper, the Huangshui River above the center of Minhe county was taken as the research area, and the Pinus tabulaeformis and shrubs were taken as the main forest land types of slope ecological restoration. First, based on the law of forest land variation, the construction scales of slope ecological restoration in different periods were identified. The influence of slope ecological restoration on runoff was then quantitatively evaluated by using a distributed hydrological model. Second, the future climate scenarios of five general circulation models (GCMs) under three representative concentration pathways (RCPs) (i.e., RCP2.6, RCP4.5, and RCP8.5) from 2021 to 2050 were selected and modified by model integration. Combined with the slope ecological restoration scenarios, the influence of slope ecological restoration on runoff under future climate scenarios was explored. The results showed that the effect of slope ecological restoration was significant. Compared with 1980, the area of slope ecological restoration increased by 24% in 2017. Under the present climate conditions (1960–2017), different periods of slope ecological restoration have an effect on the process of runoff in the wet season (June, July, August, and September) and dry season (January, February, March, and December), which eliminates the maximum, replenishes the minimum, and reduces the variability of runoff processes in the watershed. Under the future climate scenario (2021–50), slope ecological restoration will reduce runoff. On the other hand, climate change will increase runoff, and the combination of the two effects will have a certain offsetting effect. On the whole, comparing the influence of slope ecological restoration on the runoff process with that of climate change in different seasons, due to the main influence of slope ecological restoration, the runoff decreased by about 55% in the temperate season (April, May, October, and November), and increased by about 50% in the dry season or wet season due to the main influence of future climate scenarios.

Changes in water and sediment conditions in the Upper Yellow River Basin (UYRB), which contains the ecological barrier and “water tower” of the entire Yellow River Basin, directly affect the development of the downstream ecological environment. In this study, the impacts of recent and potential land use and climate changes on runoff and sediment load were investigated through statistical analysis, land-use maps, scenario estimations, and hydrological modelling. The temporal trends and abrupt changes in hydro-meteorological elements from 1957-2010 were analysed using linear regressions and moving-t tests. Transformations in land use from 1990 to 2014 were determined using a transfer matrix analysis. The back propagation neural network was constructed to modify and integrate several general climate models, and it projected the climate change evolution characteristics in the UYRB from 2021 to 2100 under different emission scenarios. On this basis, the effects of recent and potential land use and climate changes on runoff and sediment load were quantified using the Soil and Water Assessment Tool (SWAT) hydrological model and fifteen climate scenarios, respectively. The results show a significant decreasing trends for both runoff and sediment loads with warmer and wetter climate conditions in the past 50 years. An abrupt change in runoff occurred in 1990, and a notable change in sediment load occurred in 2000 which was defined as the dividing year of the study period. The warm and wet climate characteristics of the UYRB will continue from 2021 to 2100. Over the past 20 years, the transformation of land use in the URYB has intensified. Therefore, land use changes between 1990 and 2000 show significant increasing wetland trends and decreasing bare land and grassland trends. The SWAT simulation results indicated that climate changes have had a more significant impact on runoff than land use changes in the past 20 years, increasing runoff by 6.32%. Both land use and climate changes have great impacts on sediment load reduction. For the next 80 years, the potential land use change demonstrated greater impacts on runoff and sediment load than climate changes. The runoff and sediment load exhibited different trends under various climate conditions and emission scenarios. The results obtained in this study can provide useful information for water resource management, soil and water conservation, and ecological protection in the UYRB.