Establishing and modeling the causality relationship of hydro-climatic and land cover change variables with water quality over Lake Tana, Ethiopia

Abstract

Lakes, the most widespread inland water bodies in the globe, are highly susceptible to change in trophic state due to external factors. Changing hydro-climatic conditions and land cover changes (LCC) can cause lake water quality deterioration. This study establishes the quantitative relationship between variability in the water quality index and changes in hydro-climatic and LCC variables. Water quality is represented by the Forel-ule index (FUI) whereas the hydro-climatic variables considered in this study are lake bottom layer temperature (lblt), lake total layer temperature (ltlt), precipitation, runoff, evaporation, lake skin temperature (lskt), surface wind speed and air temperature. The LCC is quantified by lower and higher level leaf area index (Lv-lai and Hv-lai). FUI has a positive relationship with surface wind speed, precipitation, runoff, ltlt, lblt, and LCC and a negative relationship with evaporation, lskt, and air temperature with 95% confidence level over most parts of the Lake. The temporal correlation is also apparent from the long-term trend pattern. A significant decreasing trend is observed in FUI and lake bottom layer temperature (lblt). In contrast, an insignificant increasing trend is observed in air temperature and lake skin temperature (lskt). The changes in LCC, runoff, precipitation, and surface wind speed is insignificant between 2000 and 2020. Moreover, the phase composites of FUI and hydro-climatic and LCC variables derived from multichannel singular spectrum analysis (MSSA) show strong seasonal modulation of water quality by hydro-climatic and LCC variables. The annual cycle represented by the first two eigenmodes (except wind speed which is represented by the second and third eigenmodes) accounts for between 27.41% (wind speed) to 52.32% (precipitation) of the total joint spatiotemporal variability of FUI and the driving variables. The convergent cross-mapping (CCM) analysis shows that cross-map skill (ρ2) is increased with increasing library length (L) and time delay (τ), which suggests significant causal effects of hydro-climatic and LCC variables on FUI and the lagged causation is consistent with maximum values of ρ2. The significant feedback of FUI to changes in hydro-climatic and LCC variables shows the possibility of hindcast/forecast of the historical/future status of water quality from hydro-climatic and LCC variables. As a result, a multivariate nonlinear regression model (MNWQFM) is developed to forecast the lake water quality index from the hydro-climatic and LCC variables. The model has high performance with R2 of 83.6% and root means square error (RMSE) of 0.15 in FUI.