Multi-model assessment of glacio-hydrological changes in central Karakoram,Pakistan

The multi-model assessment of glaciohydrological regimes can enhance our understanding of glacier response to climate change. This improved knowledge can uplift our computing abilities to estimate the contributing components of the river discharge. This study examined and compared the hydrological responses in the glacier-dominated Shigar River basin(SRB) under various climatic scenarios using a semi-distributed Modified Positive Degree Day Model(MPDDM) and a distributed Glacio-hydrological Degree-day Model(GDM). Both glacio-hydrological models were calibrated and validated against the observed hydro-meteorological data from 1988-1992 and 1993-1997. Temperature and precipitation data from Shigar and Skardu meteorological stations were used along with field estimated degree-day factor, temperature, and precipitation gradients. The results from both models indicate that the snow and ice melt are vital contributors to sustain river flow in the catchment. However, MPDDM estimated 68% of rain and baseflow contribution to annual river runoff despite low precipitation during the summer monsoon, while GDM estimated 14% rain and baseflow contribution. Likewise, MPDDM calculated 32%, and GDM generated 86% of the annual river runoff from snow and ice melt. MPDDM simulated river discharge with 0.86 and 0.78 NSE for calibration and validation, respectively. Similarly, GDM simulated river discharge with improved accuracy of 0.87 for calibration and 0.84 NSE for the validation period. The snow and ice melt is significant in sustaining river flow in the SRB, and substantial changes in melt characteristics of snow and ice are expected to have severe consequences on seasonal water availability. Based on the sensitivity analysis, both models' outputs are highly sensitive to the variation in temperature. Furthermore, compared to MPDDM, GDM simulated considerable variation in the river discharge in climate scenarios, RCP4.5 and 8.5, mainly due to the higher sensitivity of GDM model outputs to temperature change. The integration of an updated melt module and two reservoir baseflow module in GDM is anticipated to advance the representation of hydrological components, unlike one reservoir baseflow module used separately in MPDDM. The restructured melt and baseflow modules in GDM have fundamentally enriched our perception of glacio-hydrological dynamics in the catchment.     

The influence of three mangrove species on the distribution of inorganic nitrogen and phosphorus in the Quanzhou Bay estuarine wetland soils

This study aims to investigate the effects of region and three regional dominated mangrove species (Avicennia marina, Aegiceras corniculatum and Kandelia candel) on the distribution of inorganic nitrogen and phosphorus. Measurement of the inorganic nitrogen and phosphorus and enzymatic activities was carried out in soils covered by three mangrove species in the Quanzhou Bay estuarine wetlands, a typical coastal wetland in China. Species with a higher biomass in upstream and midstream absorb more nitrogen from soils, and the retention of the available phosphorus in the soils of different regions causes the regional variation of phosphorus. In areas dominated by A. marina, nitrate nitrogen is lower while available phosphorus is higher. Meanwhile, nitrate nitrogen and available phosphorus are higher in the soils covered by K. candel. Moreover, all three species affect the elemental and enzymic stoichiometry. The mangrove species influences the diversity of the elemental and enzymic stoichiometric relationship through differential microenvironments, which induce the biodiversity of wetland ecosystems. Thus, this study may facilitate a better understanding of the transformation ability of mangroves to nitrogen and phosphorus and will therefore be beneficial for providing a basis for the ecological restoration of estuarine wetlands.     

Examining the rainfall–topography relationship using non-stationary modelling technique in semi-arid Aseer region,Saudi Arabia

The mountainous region of Aseer, corresponding to the Afromontane phytogeographic region, is an eco-sensitive zone and has complex relationship between topography and rainfall. The region is located inland of the red sea escarpment edge in the west. Therefore, rainfall can occur during any month of the year in the mountain of the high Aseer region when moist air forces up the escarpment from the red sea. Monitoring the rainfall data and its topographical elevation variable in Aseer region is an essential requirement for feasible and accurate rainfall-based data for different applications, such as hydrological and ecological resource management in rugged terrain and remote areas. The relationship of elevation and rainfall are spatially non-stationary, non-linear, scale dependent, and often modelled by conventional regression models. Therefore, a local modelling technique, geographically weighted regression (GWR), was applied to deal with non-stationary, non-linear, scale-dependent problems. The GWR using topoclimatic data (elevation and rainfall) to analyse the cumulative rainfall data for rainy months (March to June) of the 4 years estimated from CHIRPS (Climate Hazards Group InfraRed Precipitation with Stations) product for Aseer region. The bandwidth (scale-size) of the Aseer region rainfall–elevation relationship has stabilised at round off 12 km. By selecting the suitable bandwidth, the spatial pattern of the rainfall–elevation relationship was significantly enhanced by using the GWR than the traditional ordinary least squares (OLS) regression model. GWR local modelling techniques estimated well in terms of accuracy, predictive power and decreased residual autocorrelation. Additionally, GWR assesses the significance of local statistic at each location and identified the location of spatial clusters with local regression coefficients significantly improved as compared with global OLS model, thereby highlighting local variations. Therefore, the GWR, local modelling approach managed to produce more accurate estimates by taking into account local characteristics.