Multivariate framework for the assessment of key forcing to Lake Malawi level variations in non-stationary frequency analysis

Abstract

Lake Malawi in south eastern Africa is a very important freshwater system for the socio-economic development of the riparian countries and communities. The lake has however experienced considerable recession in the levels in recent years. Consequently, frequency analyses of the lake levels premised on time-invariance (or stationarity) in the parameters of the underlying probability distribution functions (pdfs) can no longer be assumed. In this study, the role of hydroclimate forcing factors (rainfall, lake evaporation, and inflowing discharge) and low frequency climate variability indicators (e.g., El Nino Southern Oscillation-ENSO and the Indian Ocean Dipole Mode-IODM) on lake level variations is investigated using a monthly mean lake level dataset from 1899 to 2017. Non-stationarity in the lake levels was tested and confirmed using the Mann-Kendall trend test (α = 0.05 level) for the first moment and the F test for the second moment (α = 0.05 level). Change points in the series were identified using the Mann-Whitney-Pettit test. The study also compared stationary and non-stationary lake level frequency during 1961 to 2004, the common period where data were available for all the forcing factors considered. Annual maximum series (AMS) and peak over threshold (POT) analysis were conducted by fitting various candidate extreme value distributions (EVD) and parameter fitting methods. The Akaike information criteria (AIC), Bayesian information criteria (BIC), deviance information criteria (DIC), and likelihood ratios (RL) served as model evaluation criteria. Under stationary conditions, the AMS when fitted to the generalized extreme value (GEV) distribution with maximum likelihood estimation (MLE) was found to be superior to POT analysis. For the non-stationary models, open water evaporation as a covariate of the lake levels with the GEV and MLE was found to have the most influence on the lake level variations as compared with rainfall, discharge, and the low frequency climatic forcing. The results are very critical in flood zoning especially with various planned infrastructural developments around the lakeshore.

Peaks-over-threshold model in flood frequency analysis: a scoping review

“…However, in assessing the AM and POT time series data, a trend or a jump may be found in many cases, which undermines the stationary assumption (Ishak and Rahman 2015;Ishak et al 2013). The identified anomalies may be statistically significant or insignificant, which may be due to climate change or other reasons such as land use changes (Burn et al 2010;Cunderlik et al 2007;Cunderlik and Ouarda 2009;Ngongondo et al 2013;Ngongondo, Zhou and Xu 2020;Silva et al 2012;Zhang, Duan and Dong 2019). Recent application of POT with non-stationarity is proposed by Lee, Sim and Kim (2019) for extreme rainfall analysis.…”

“…MOM and PWM were preferred byHu et al (2020) andMetzger et al (2020), respectively. ML has been widely adopted in many studies despite a limited sample size(Martins & Stedinger 2001;Mostofi Zadeh et al 2019;Nagy et al 2017;Ngongondo, Zhou and Xu 2020; Zhao et al 2019a, b; Zhou et al 2017a, b, c) Madsen et al (1997). compared the performance of parameter estimator between AM and POT-GP models.…”

Land use/land cover dynamics, trade-offs and implications on tropical inland shallow lakes’ ecosystems’ management: Case of Lake Malombe, Malawi

“…Magadza (2010) also had similar findings in Lake Kariba and Zambezi River Valley. The main reason for decreased waterbody in Figure 4b is the prolonged drought, siltation, and rainfall decline as temperatures in the catchment increase (Likoya, 2019;Ngongondo et al, 2020;Power, 2010).…”

Probabilistic interval estimation of design floods under non-stationary conditions by an integrated approach

“…In recent years, several methods were used to describe the variation of parameters and weighting coefficients in distributions of hydrological series, such as single-type distributions (Gilroy & McCuen 2012), two-component mixture distributions (Bayazit 2015;Yan et al 2017b) and timevarying moments (Xiong et al 2015a;Yu et al 2018). Additionally, the change point and trend change detection methods such as the Pettitt test (Pettitt 1979;Reeves et al 2007;Ngongondo et al 2020), the Mann-Kendall test (Chebana et al 2013) and the trend-free pre-whitening method (Yue et al 2002) were applied to detect the variability of hydrological series (Villarini & Smith 2010;Sȩn 2011;Rougé et al 2013). Although the change point and trend change detection methods can be used to detect the hydrological non-stationarity, they cannot quantify the effects of possible physical factors on the non-stationarity of hydrological series (López & Francés 2013;Villarini & Strong 2014;Zhang et al 2015;Wu et al 2017;Liang et al 2018;Su & Chen 2019;Bian et al 2020).…”