Considering the high random and non-static property of the rainfall-runoff process, lots of models are being developed in order to learn about such a complex phenomenon. Recently, Machine learning techniques such as the Artificial Neural Network (ANN) and other networks have been extensively used by hydrologists for rainfall-runoff modelling as well as for other fields of hydrology. However, deep learning methods such as the state-of-the-art for LSTM networks are little studied in hydrological sequence time-series predictions. We deployed ANN and LSTM network models for simulating the rainfall-runoff process based on flood events from 1971 to 2013 in Fen River basin monitored through 14 rainfall stations and one hydrologic station in the catchment. The experimental data were from 98 rainfall-runoff events in this period. In between 86 rainfall-runoff events were used as training set, and the rest were used as test set. The results show that the two networks are all suitable for rainfall-runoff models and better than conceptual and physical based models. LSTM models outperform the ANN models with the values of $R^2$ and $NSE$ beyond 0.9, respectively. Considering different lead time modelling the LSTM model is also more stable than ANN model holding better simulation performance. The special units of forget gate makes LSTM model better simulation and more intelligent than ANN model. In this study, we want to propose new data-driven methods for flood forecasting.

Runoff reduction in most river basins in China has become a hotpot in recent years. The Gushanchuan river, a primary tributary of the middle Yellow river, Northern China, showed a significant downward trend in the last century. Little is known regarding the relative contributions of changing environment to the observed hydrological trends and response on the runoff generation process in its watershed. On the basis of observed hydrological and meteorological data from 1965–2010, the Mann-Kendall trend test and climate elasticity method were used to distinguish the effects of climate change and human activities on runoff in the Gushanchuan basin. The results indicate that the runoff in the Gushanchuan Basin has experienced significant declines as large as 77% from 1965 to 2010, and a mutation point occurred around 1997; the contribution rate of climate change to runoff change is 12.9–15.1%, and the contribution rate of human activities to runoff change is 84.9–87.1%. Then we divided long-term data sequence into two stages around the mutation point, and analyzed runoff generation mechanisms based on land use and cover changes (LUCC). We found that the floods in the Gushanchuan Basin were still dominated by Excess-infiltration runoff, but the proportion in 1965–1997 and 1998–2010 decreased gradually (68.46% and 45.83% in turn). The proportion of Excess-storage runoff and Mixed runoff has increased, which means that the runoff is made up of more runoff components. The variation law of the LUCC indicates that the forest area increased by 49.61%, the confluence time increased by 50.42%, and the water storage capacity of the watershed increased by 30.35%.

Pakistan is water stressed, and its water resources are vulnerable due to uncertain climatic changes. Uncertainties are inherent when it comes to the modeling of water resources. The predicted flow variation in the Kunhar River Basin was modeled using the statistically decreased high-resolution general circulation model (GCM) as an input for the Hydrologiska Byråns Vattenbalansavdelning (HBV) model to assess the hydrological response of the Kunhar River Basin under prevailing climate changes. The model’s best performance during the calibration and validation stages was obtained with a regular 0.87 and 0.79 Nash–Sutcliffe efficiency in the basin, respectively. Under the high-end emission scenario, a 122% increase was expected in evapotranspiration in the rising season of months during the winter period 2059–2079, and such developments were attributed to an immense increase in liquid precipitation and temperature. The model’s output reflects a potential for basin stream flow in terms of increasing liquid precipitation up to 182% at the beginning of the monsoon season in the period 2059–2079 in the scenario of high-end emissions. Moreover, the study produced possible uncertainties in high-elevation zones, where the modeling of a catchment can lead to typical snow ablation and accumulation in future projections. This study revealed that the precipitation rate will increase annually, resulting in an increase in the summer stream flow over the basin, though snow is hardly expected to accumulate in the basin’s future climate.

An experiment was conducted in an oasis area in northwest China to assess the coupling effects of water and nitrogen supply through drip irrigation on photosynthetic characteristics, nitrogen uptake, and yield of sunflower (Helianthus annuus L.), as well as the nitrate distribution in the root-zone soil. The experiment included three irrigation levels (210 [W1], 300 [W2], and 390 [W3] mm), three nitrogen levels (162 [N1], 232 [N2], and 302 [N3] kg/hm2), and control treatment (no fertilization during whole growth period and only irrigated at the budding stage). A nitrogen content over-accumulation in the soil was observed under the low irrigation amount with high fertilizer supply, which cannot enhance the sunflowers’ absorption of nitrogen. Excessive irrigation caused leach of the soil nitrogen, reduced nitrogen content in the root-zone soil (0-40 cm) and limited nitrogen uptake by the crop. Although low irrigation amount with high fertilizer supply can increase the nitrogen content in the soil, it cannot enhance the sunflowers’ absorption of nitrogen. At the vegetative stage of sunflower growth, the accumulation of nitrogen in the organs was mainly concentrated in the leaves, while it was transferred to the flower disk at the reproductive growth stage. Reasonable coupling of water and nitrogen improved the transport of nitrogen from leaves and stems to the flower disk aromoted the formation of yield components. Six regression equations were established with irrigation and fertilization amount as independent variables, and seed rate, seed weight per flowerdisk, 1000 grain weight, yield, water use efficiency and nitrogen partial factor productivity as dependent variables, respectively. Multiple regression and spatial analysis suggested that the irrigation amount of 241.62- 253.35 mm and the fertilization application of 202.02-209.40 N kg/hm2 was a good irrigation strategy, under which all six factors exceed 75% of their maxima, and the yield of sunflower reached 3 229.3 kg/hm2. Keywords: fertilization, Helianthus annuus L., irrigation management, nitrogen transformation, nutrient uptake, Xinjiang DOI: 10.25165/j.ijabe.20211405.6399 Citation: Wang T Y, Wang Z H, Wu Q, Zhang J Z, Quan L S, Fan B H, et al. Coupling effects of water and nitrogen on photosynthetic characteristics, nitrogen uptake, and yield of sunflower under drip irrigation in an oasis. Int J Agric & Biol Eng, 2021; 14(5): 130–141.