Strengthening Flood and Drought Risk Management Tools for the Lake Chad Basin

Abstract

Lake Chad is extremely sensitive to climate variability because it is a shallow inland lake, and about 97.5% of its water supply depends on the Chari-Logone River System and other tributaries. Any increase or decrease in lake volume inflow means a substantial increase or decrease in lake area. Droughts in the Sahel Region and within the basin after the 1970s had great impact on discharges of different tributaries, which led to a drastic decrease of water inflow in the lake, as well as significant seasonal and inter-annual variation of the lake area over the last 50 years. Information gaps about the water system and uncertainties about climate variability and change remain a challenge. Hydrological extremes, both floods and droughts, present a threat to agriculture and water resource management within the Lake Chad Basin. Drought and flood monitoring over the basin is difficult because of the shortage of observational data, both historic and in real time. Satellite remote sensing and hydrological modelling are techniques used to compensate for the data collection shortcomings of the region. The Africa Flood and Drought Monitoring (AFDM) provides drought and flood monitoring, and short-term and seasonal forecasting that combine climate prediction, hydrological modelling and remote sensing data in the sub-Saharan African continent. For the Lake Chad Region, the system was adapted with higher resolution to provide near-real-time water levels, as well as short-term forecast of flood risks, as well as medium-term forecasts of drought hazards and long-term projections of climate change impacts. Preliminary results are very encouraging; the system will continue to be updated, tested and validated to enable its operational use by decision-makers at all levels.

Appendix

A.1. Validation Datasets

a. Precipitation

GPCP — Global Precipitation Climatology Project

We use the latest version (v2.3) of the GPCP monthly analysis. This improves the homogeneity of the previous version, especially since 2002, through corrections of the cross-calibration of satellite data and updates to the gauge analysis. The dataset is a merger of various satellite-based estimates globally, combined with the precipitation gauge analyses over land from the Global Precipitation Climatology Centre (GPCC). The satellite-based estimates are a combination of passive microwave estimates over ocean, passive microwave estimates over land and estimates from IR/microwave sounders, contributing at higher latitudes (above 40^o latitude).

TMPA — TRMM Multisensor Precipitation Analysis

We use the latest version (V7) of the TMPA. The TMPA is a calibration-based sequential scheme for combining precipitation estimates from multiple satellites, as well as gauge analyses where feasible, at fine scales (0.25° × 0.25° and 3 hourly). TMPA is available both after and in real time, based on calibration by the TRMM combined instrument and TRMM microwave imager precipitation products, respectively. Only the after-real-time product incorporates gauge data. The dataset covers the latitude band 50°N–S for the period from 1998 to the delayed present. The data currently contains two products, 3 hourly combined microwave-IR estimates (with gauge adjustment) and monthly combined microwave-IR-gauge estimates of precipitation computed on quasi-global grids about 2 months after the end of each month starting in January 1998. We use the 3B43_7 monthly version of the dataset, which incorporates gauge adjustments, at 0.25° resolution.

b. Soil Moisture

SMAP — Soil Moisture Active Passive

The NASA Soil Moisture Active Passive (SMAP) mission launched in January 2015 was designed for global mapping of soil moisture at a 10-km spatial resolution with a 2–3-day revisit time under both clear and cloudy sky conditions. This improves on the resolution relative to AMSR-E (25 km) and SMOS (50 km) by combining an L-band radar (high resolution 1–3 km, lower accuracy) and an L-band radiometer (low resolution 40 km, higher accuracy), as well as retrievals for a wider range of vegetation conditions and for the top 5 cm of the soil. SMAP products also include level 4 (L4) root-zone estimates by merging SMAP observations with land surface model estimates via assimilation extending the utility of the data. Unfortunately, the SMAP radar failed in July 2015 thus restricting soil moisture products to the 40 km resolution of the radiometer. Here we use monthly mean values that are calculated in the AFDM based on 3-day moving average data, which are referred to as SMAP-MA.

AMSR - E  +  VOD

This dataset is based on a retrieval algorithm for daily global soil moisture and vegetation optical depth (VOD) derived from dual-polarized AMSR-E brightness temperatures at 10.7 GHz. It uses a simplified radiative transfer model that assumes (1) the surface to be retrieved is vegetated soil only and (2) the vegetated soil consists of a smooth bare soil surface plus a layer of vegetation. The model approximates the output from generally used, but more complex and more rigorously parameterized radiative transfer models quite well, irrespective of the vegetation density. By incorporating both soil roughness parameters and to-be-retrieved VOD implicitly, the algorithm simplifies the retrieval process since accurate prior knowledge of the global soil roughness information is unavailable at AMSR-E frequencies and footprint scales. The data are available globally, for 2002–2009, at 0.25°, daily resolution.

c. Evapotranspiration

GLEAM—Global Land Evaporation Amsterdam Model

We use the latest version of the GLEAM dataset, V3.2a, which is a global dataset spanning 1980–2017 at 0.5°, daily resolution. The dataset is based on reanalysis net radiation and air temperature, satellite and gauged-based precipitation, VOD, soil moisture and snow water equivalent. Components of evapotranspiration (transpiration, canopy interception, soil evaporation, open water evaporation and sublimation) are calculated separately. The Priestley and Taylor equation is used to calculate potential evaporation based on reanalysis surface net radiation and near-surface air temperature. Estimates of potential evaporation for the land fractions of bare soil, tall canopy and short canopy are converted into actual evaporation using a multiplicative evaporative stress factor based on observations of microwave Vegetation Optical Depth (VOD) and estimates of root-zone soil moisture. The latter is calculated using a multi-layer running-water balance. To try to correct for random forcing errors, satellite retrievals of surface soil moisture are also assimilated into the soil profile. Interception loss is calculated separately in using a Gash analytical model. Finally, estimates of actual evaporation for water bodies and regions covered by ice and/or snow are obtained using an adaptation of the Priestley and Taylor equation. We also use the surface and root-zone soil moisture calculated by GLEAM.

d. Terrestrial Water Storage

GRACE — Gravity Recovery and Climate Experiment

The GRACE mission has been monitoring changes in the Earth’s gravity field since its launch in 2002 by measuring the distance between two orbiting satellites. Variations in these fields can be attributed to changes in terrestrial water storage after removal of atmospheric and ocean bottom pressure changes. We use the JPL RL05 dataset of approximately 30-day Total Water Storage (TWS) anomalies. We also use the JPL RL05-based time series for the Lake Chad Basin using a 300 km Gaussian half-width averaging kernel that is available from the University of Colorado.