Application of Entropy Spectral Method for Streamflow Forecasting in Northwest China

Streamflow forecasting is vital for reservoir operation, flood control, power generation, river ecological restoration, irrigation and navigation. Although monthly streamflow time series are statistic, they also exhibit seasonal and periodic patterns. Using maximum Burg entropy, maximum configurational entropy and minimum relative entropy, the forecasting models for monthly streamflow series were constructed for five hydrological stations in northwest China. The evaluation criteria of average relative error (RE), root mean square error (RMSE), correlation coefficient (R) and determination coefficient (DC) were selected as performance metrics. Results indicated that the RESA model had the highest forecasting accuracy, followed by the CESA model. However, the BESA model had the highest forecasting accuracy in a low-flow period, and the prediction accuracies of RESA and CESA models in the flood season were relatively higher. In future research, these entropy spectral analysis methods can further be applied to other rivers to verify the applicability in the forecasting of monthly streamflow in China

Modeling NDVI Using Joint Entropy Method Considering Hydro-Meteorological Driving Factors in the Middle Reaches of Hei River Basin

Terrestrial vegetation dynamics are closely influenced by both hydrological process and climate change. This study investigated the relationships between vegetation pattern and hydro-meteorological elements. The joint entropy method was employed to evaluate the dependence between the normalized difference vegetation index (NDVI) and coupled variables in the middle reaches of the Hei River basin. Based on the spatial distribution of mutual information, the whole study area was divided into five sub-regions. In each sub-region, nested statistical models were applied to model the NDVI on the grid and regional scales, respectively. Results showed that the annual average NDVI increased at a rate of 0.005/a over the past 11 years. In the desert regions, the NDVI increased significantly with an increase in precipitation and temperature, and a high accuracy of retrieving NDVI model was obtained by coupling precipitation and temperature, especially in sub-region I. In the oasis regions, groundwater was also an important factor driving vegetation growth, and the rise of the groundwater level contributed to the growth of vegetation. However, the relationship was weaker in artificial oasis regions (sub-region III and sub-region V) due to the influence of human activities such as irrigation. The overall correlation coefficient between the observed NDVI and modeled NDVI was observed to be 0.97. The outcomes of this study are suitable for ecosystem monitoring, especially in the realm of climate change. Further studies are necessary and should consider more factors, such as runoff and irrigation

Spatial Optimization of Agricultural Land Use Based on Cross-Entropy Method

An integrated optimization model was developed for the spatial distribution of agricultural crops in order to efficiently utilize agricultural water and land resources simultaneously. The model is based on the spatial distribution of crop suitability, spatial distribution of population density, and agricultural land use data. Multi-source remote sensing data are combined with constraints of optimal crop area, which are obtained from agricultural cropping pattern optimization model. Using the middle reaches of the Heihe River basin as an example, the spatial distribution of maize and wheat were optimized by minimizing cross-entropy between crop distribution probabilities and desired but unknown distribution probabilities. Results showed that the area of maize should increase and the area of wheat should decrease in the study area compared with the situation in 2013. The comprehensive suitable area distribution of maize is approximately in accordance with the distribution in the present situation; however, the comprehensive suitable area distribution of wheat is not consistent with the distribution in the present situation. Through optimization, the high proportion of maize and wheat area was more concentrated than before. The maize area with more than 80% allocation concentrates on the south of the study area, and the wheat area with more than 30% allocation concentrates on the central part of the study area. The outcome of this study provides a scientific basis for farmers to select crops that are suitable in a particular area

The effect of afforestation on moist heat stress in Loess Plateau, China

Study region: Loess Plateau (LP), China Study focus: This study aimed to research whether and to what degree afforestation contributes to the variations in moist heat stress in the study area. Here, wet bulb, temperature (Tw) was used to quantify the moist heat stress. Subsequently, The Weather Research and Forecasting model (WRF) is applied to simulate the modulation of climate change related to afforestation during 2001–2015. Based on the analysis of energy fluxes, we identified the biogeophysical mechanism of afforestation impact on moist heat stress. New hydrological insights for the region: Since the operation of the “Grain-to-Green” program, LP has experienced widespread afforestation which perturbs energy and water fluxes, affecting regional climate regimes. The forest expansion increases relative humidity but cools the regional temperature. As a significant combined climate factor, the average moist heat stress decreases with the magnitude of − 0.1∼− 0.3 °C in central LP. While the decrease rate of Tw is slower than near-surface temperature. It is worth noting that, an increased signal occurs in the maximum Tw (almost 0.2 °C in eastern and northeastern LP), which might expose humans to the risk of moist heat stress. By the mechanistic analysis, the research shows that the near-surface temperature and sensible heat flux are dominant driving factors for the change of Tw. Furthermore, the subsidence of the planetary boundary layer enhances moist heat stress. Overall, afforestation's effects on land surface-atmosphere interaction are non-negligible and the moist heat stress should be accounted for in climate change adaptation strategies

Application of Ground-Based Microwave Radiometer in Retrieving Meteorological Characteristics of Tibet Plateau

The characteristics of plateau precipitation and atmosphere, once accurately and comprehensively understood, can be used to inform sound air–water resource development practices. In this study, atmospheric exploration of the Tibet Plateau (TP) was conducted using ground-based microwave radiometer (MWR) data collected during the East Asian summer monsoon. Atmospheric temperature, pressure, humidity, and other variables were gathered under clear-sky, cloudy-sky, and rainy-sky conditions. Statistical characteristics of the air parcel height and stability/convection indices such as convective available potential energy (CAPE) and convective inhibition (CIN) were investigated, with a special focus on the rainy-sky condition. Two retrieval applications for characterizing precipitation, namely short-term precipitation forecast and quantitative precipitation estimation were presented. Results showed that CAPE values in the Darlag region reached extremes around 18:00–20:00 (UTC+8) for cloudy-sky and rainy-sky conditions with corresponding peaks of about 1046.56 J/kg and 703.02 J/kg, respectively. When stratiform or convective–mixed precipitation occurs, the precipitable water vapor (PWV) and CAPE values were generally greater than 1.7 cm and 1000 J/kg, respectively. CAPE values are likely to decrease before the occurrence of precipitation due to the release of the latent heat in the atmosphere