Impacts of Land-use and Land-cover Changes on River Runoff in Yellow River Basin for Period of 1956–2012

River runoff is affected by many factors, including long-term effects such as climate change that alter rainfall-runoff relationships, and short-term effects related to human intervention(e.g., dam construction, land-use and land-cover change(LUCC)). Discharge from the Yellow River system has been modified in numerous ways over the past century, not only as a result of increased demands for water from agriculture and industry, but also due to hydrological disturbance from LUCC, climate change and the construction of dams. The combined effect of these disturbances may have led to water shortages. Considering that there has been little change in long-term precipitation, dramatic decreases in water discharge may be attributed mainly to human activities, such as water usage, water transportation and dam construction. LUCC may also affect water availability, but the relative contribution of LUCC to changing discharge is unclear. In this study, the impact of LUCC on natural discharge(not including anthropogenic usage) is quantified using an attribution approach based on satellite land cover and discharge data. A retention parameter is used to relate LUCC to changes in discharge. We find that LUCC is the primary factor, and more dominant than climate change, in driving the reduction in discharge during 1956–2012, especially from the mid-1980 s to the end-1990 s. The ratio of each land class to total basin area changed significantly over the study period. Forestland and cropland increased by about 0.58% and 1.41%, respectively, and unused land decreased by 1.16%. Together, these variations resulted in changes in the retention parameter, and runoff generation showed a significant decrease after the mid-1980 s. Our findings highlight the importance of LUCC to runoff generation at the basin scale, and improve our understanding of the influence of LUCC on basin-scale hydrology.

Dynamic Diurnal Changes in Green Algae Biomass in the Southern Yellow Sea Based on GOCI Images

Macroalgae blooms of Ulva prolifera have occurred every summer in the southern Yellow Sea since 2007,inducing severe ecological problems and huge economic losses.Genesis and secular movement of green algae blooms have been well monitored by using remote sensing and other methods.In this study,green algae were detected and traced by using Geostationary Ocean Color Imager(GOCI),and a novel biomass estimation model was developed from the relationship between biomass measurements and previously published satellite-derived biomass indexes.The results show that the green algae biomass can be determined most accurately with the biomass index of green algae for GOCI(BIGAG),which is calculated from the Rsurf data that had been atmospherically corrected by ENVI/QUAC method.For the first time,dynamic changes in green algae biomass were studied over an hourly scale.Short-term biomass changes were highly influenced by Photosynthetically Available Radiation(PAR)and tidal phases,but less by sea surface temperature variations on a daily timescale.A new parameter of biomass changes(PBC),calculated by the ratio of the biomass growth rate to movement velocity,could provide an effective way to assess and forecast green tide in the southern Yellow Sea and similar areas.

The Influence of Weather and Climate Variability on Groundwater Quality in Zanzibar

Climate change and variability have been inducing a broad spectrum of impacts on the environment and natural resources including groundwater resources. The study aimed at assessing the influence of weather, climate variability, and changes on the quality of groundwater resources in Zanzibar. The study used the climate datasets including rainfall (RF), Maximum and Minimum Temperature (Tmax and Tmin), the records acquired from Tanzania Meteorological Authority (TMA) Zanzibar office for 30 (1989-2019) and 10 (2010-2019) years periods. Also, the Zanzibar Water Authority (ZAWA) monthly records of Total Dissolved Solids (TDS), Electrical Conductivity (EC), and Ground Water Temperature (GWT) were used. Interpolation techniques were used for controlling outliers and missing datasets. Indeed, correlation, trend, and time series analyses were used to show the relationship between climate and water quality parameters. However, simple statistical analyses including mean, percentage changes, and contributions to the annual and seasonal mean were calculated. Moreover, t and paired t-tests were used to show the significant changes in the mean of the variables for two defined periods of 2011-2015 and 2016-2020 at p ≤ 0.05. Results revealed that seasonal variability of groundwater quality from March to May (MAM) has shown a significant change in trends ranging from 0.1 to 2.8 mm/L/yr, 0.1 to 2.8 μS/cm/yr, and 0.1 to 2.0℃/yr for TDS, EC, and GWT, respectively. The changes in climate parameters were 0.1 to 2.4 mm/yr, 0.2 to 1.3℃/yr and 0.1 to 2.5℃/yr in RF, Tmax, and Tmin, respectively. From October to December (OND) changes in groundwater parameters ranged from 0.2 to 2.5 mm/L/yr 0.1 to 2.9 μS/cm/yr, and 0.1 to 2.1℃/yr for TDS, EC, and GWT, whereas RF, Tmax, and Tmin changed from 0.3 to 1.8 mm/yr, 0.2 to 1.9℃/yr and 0.2 to 2.0℃/yr, respectively. Moreover, the study has shown strong correlations between climate and water quality parameters in MAM and OND. Besides, the paired correlation has shown significant changes in all parameters except the rainfall. Conclusively, the study has shown a strong influence of climate variability on the quality of groundwater in Zanzibar, and calls for more studies to extrapolate these results throughout Tanzania.

Test for autocorrelation change in discretely observed Ornstein-Uhlenbeck processes driven by Lévy processes

In this paper, we consider the problem of testing for an autocorrelation change in discretely observed Ornstein-Uhlenbeck processes driven by Levy processes. For a test, we propose a class of test statistics constructed by an iterated cumulative sums of squares of the difference between two adjacent observations. It is shown that each of the test statistics weakly converges to the supremum of the square of a Brownian bridge. The test statistics are evaluated by some empirical results.

On nonparametric change point estimator based on empirical characteristic functions

We propose a nonparametric change point estimator in the distributions of a sequence of independent observations in terms of the test statistics given by Huˇskov′a and Meintanis(2006) that are based on weighted empirical characteristic functions. The weight function ω(t; a) under consideration includes the two weight functions from Huˇskov′a and Meintanis(2006) plus the weight function used by Matteson and James(2014),where a is a tuning parameter. Under the local alternative hypothesis, we establish the consistency, convergence rate, and asymptotic distribution of this change point estimator which is the maxima of a two-side Brownian motion with a drift. Since the performance of the change point estimator depends on a in use, we thus propose an algorithm for choosing an appropriate value of a, denoted by a_s which is also justified. Our simulation study shows that the change point estimate obtained by using a_s has a satisfactory performance. We also apply our method to a real dataset.