Observed and simulated changes in the water balance components over Malawi, during 1971–2000

Ngongondo, Cosmo; Xu, Chong‐Yu; Tallaksen, Lena M.; Alemaw, Berhanu F.

doi:10.1016/j.quaint.2014.06.028

Cited by 28 publications

(15 citation statements)

References 45 publications

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“…The warning due to increased temperature has potential to increase the open water evaporative demand over the lake. Both temperature increases and rainfall decreases have also been reported by other studies (e.g., McSweeney et al 2010, Ngongondo et al 2015. The mean annual open water evaporation over the lake was 1290 mm (CV = 7.44%), with a statistically significant positive MK trend at α = 0.05 level for all stations as well as the average.…”

Section: Resultssupporting

confidence: 85%

“…The mean annual temperature during 1992 to 2007 is 22.4°C over the land part of the basin and 24.8°C over the water (Lyons et al 2011). Over many parts of Malawi, Ngongondo et al (2011Ngongondo et al ( , 2015 reported of rainfall declines that were not statistically Fig. 1 The extent of Lake Malawi recession showing the exposed lake bed in February, 2017.…”

Section: Study Areamentioning

confidence: 99%

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

Ngongondo

Zhou

2020

Environ Monit Assess

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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.

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Section: Resultssupporting

confidence: 85%

Section: Study Areamentioning

confidence: 99%

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

Ngongondo

Zhou

2020

Environ Monit Assess

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“…Water balance components include: precipitation, evapotranspiration, total runoff, surface runoff, groundwater flow and the soil water content. The quantification of the various water balance components of hydrological processes in a watershed remains a challenging topic [5][6][7][8], because several unknown components, e.g., evapotranspiration and soil water content, included in the water balance are difficult to measure [9]. However, the calculation of water balance components is significant for water resources assessment and management especially for water-scarce regions with regard to assessment of the impact of climate change.…”

Section: Introductionmentioning

confidence: 99%

Assessing Variation in Water Balance Components in Mountainous Inland River Basin Experiencing Climate Change

Yin

Feng

Zou

et al. 2016

Water

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Abstract:Quantification of the changes of water balance components is significant for water resource assessment and management. This paper employed the Soil and Water Assessment Tool (SWAT) model to estimate the water balance in a mountainous watershed in northwest China at different spatial scales over the past half century. The results showed that both Nash-Sutcliffe efficiency (NSE) and determination coefficient (R 2 ) were over 0.90 for the calibration and validation periods. The water balance components presented rising trends at the watershed scale, and the total runoff increased by 30.5% during 1964 to 2013 period. Rising surface runoff and rising groundwater flow contributed 42.7% and 57.3% of the total rising runoff, respectively. The runoff coefficient was sensitive to increasing precipitation and was not significant to the increase of temperature. The alpine meadow was the main landscape which occupied 51.1% of the watershed and contributed 55.5% of the total runoff. Grass land, forest land, bare land, and glacier covered 14.2%, 18.8%, 15.4%, and 0.5% of the watershed and contributed 8.5%, 16.9%, 15.9%, and 3.2% of the total runoff, respectively. The elevation zone from 3500 to 4500 m occupied 66.5% of the watershed area, and contributed the majority of the total runoff (70.7%). The runoff coefficients in the elevation zone from 1637 to 2800 m, 2800 to 3500 m, 3500 to 4000 m, 4000 to 4500 m, and 4500 to 5062 m were 0.20, 0.27, 0.32, 0.43, and 0.78, respectively, which tend to be larger along with the elevation increase. The quantities and change trends of the water balance components at the watershed scale were calculated by the results of the sub-watersheds. Furthermore, we characterized the spatial distribution of quantities and changes in trends of water balance components at the sub-watershed scale analysis. This study provides some references for water resource management and planning in inland river basins.

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“…The Potential Evaporation-Transpiration (PET) for Malawi was taken from Ngongondo et al [5], who derived an average PET of 2.9 mm/day, based on a 30-year surface water balance study.…”

Section: Meteorological Forcingmentioning

confidence: 99%

“…The basic reference population layer for Malawi, expressed in terms of number of people per pixel, is the population layer developed by WorldPop 5 . This raster layer which represents the predicted number of people per ~100 m pixel, obtained using as input census/population count datasets and mapping of settlements and applying the random forest (RF) model as described in Stevens, et al (In Press).…”

Section: Populationmentioning

confidence: 99%

Impact of modelling scale on probabilistic flood risk assessment: the Malawi case

et al. 2016

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Abstract. In the early months of 2015, destructive floods hit Malawi, causing deaths and economic losses. Flood risk assessment outcomes can be used to increase scientific-supported awareness of risk. The recent increase in availability of high resolution data such as TanDEM-X at 12m resolution makes possible the use of detailed physical based flood hazard models in risk assessment. Nonetheless the scale of hazard modelling still remains an issue, which requires a compromise between level of detail and computational efforts. This work presents two different approaches on hazard modelling. Both methods rely on 32-years of numeric weather re-analysis and rainfall-runoff transformation through a fully distributed WFLOW-type hydrological model. The first method, applied at national scale, uses fast post-processing routines, which estimate flood water depth at a resolution of about 1x1km. The second method applies a full 2D hydraulic model to propagate water discharge into the flood plains and best suites for small areas where assets are concentrated. At the 12m resolution, three hot spots with a model area of approximately 10 x10 km are analysed. Flood hazard maps obtained with both approaches are combined with flood impact models at the same resolution to generate indicators for flood risk. A quantitative comparison of the two approaches is presented in order to show the effects of modelling scale on both hazard and impact losses.

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Observed and simulated changes in the water balance components over Malawi, during 1971–2000

Cited by 28 publications

References 45 publications

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

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

Assessing Variation in Water Balance Components in Mountainous Inland River Basin Experiencing Climate Change

Impact of modelling scale on probabilistic flood risk assessment: the Malawi case

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