# Climate Extreme Indices Influencing GRACE Total Water Storage (TWS) in Semi-Arid Africa

Komi Mawulom Claude Afamondji, Ismail Mohsine, Nourelhouda Karmouda, Tarik Bouramtane, Mounia Tahiri, Ilias Kacimi, Sarah Tweed, Fatima Driouech, Marc Leblanc

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## To cite this version:

Komi Mawulom Claude Afamondji, Ismail Mohsine, Nourelhouda Karmouda, Tarik Bouramtane, Mounia Tahiri, et al. Climate Extreme Indices Influencing GRACE Total Water Storage (TWS) in Semi-Arid Africa. Journal of Arid Environments, 2025, 229, pp.105357. 10.1016/j.jaridenv.2025.105357. hal-05018045

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# Climate Extreme Indices Influencing GRACE Total Water Storage (TWS) in Semi-Arid Africa

Komi Mawulom Claude Afamondji<sup>a</sup>, Ismail Mohsine<sup>a</sup>, Nourelhouda Karmouda<sup>a</sup>, Tarik Bouramtane<sup>a</sup>, Mounia Tahiri<sup>a</sup>, Ilias Kacimi<sup>a</sup>, Sarah Tweed<sup>a,c</sup>, Fatima Driouech<sup>d</sup>, Marc Leblanc<sup>a,b,c,*</sup>

## ARTICLE INFO

### Keywords:

- Climate change indices

- Total water storage

- Drought

- Semi-arid Africa

- LSTM neural network

## ABSTRACT

This study examines the relationship between GRACE terrestrial water storage (TWS) data and climate indices related to temperature and precipitation across four semi-arid African regions: the Mediterranean (MED), West Southern Africa (WSAF), East Southern Africa (ESAF), and Madagascar (MDG). In addition to mean temperature and total precipitation, ten climate indices from the Expert Team on Climate Change Detection and Indices (ETCCDI) were analyzed for their relevance to extreme rainfall and drought. Using a Long Short-Term Memory (LSTM) neural network, the study improves TWS prediction accuracy, with feature ablation assessing the importance of each climate index. The LSTM model showed strong predictive performance ($R^2$ = 0.73–0.91), identifying the Standardized Precipitation Evapotranspiration Index (SPEI) as the key driver of TWS variability. Specifically, 12-month time scale SPEI was dominant in MED, while 6-month time scale SPEI played a crucial role in WSAF, ESAF, and MDG. Regional trend analyses revealed persistent TWS declines in MED and MDG (2002–2022) due to intensified droughts. In WSAF and ESAF, trends reversed between 2002–2011 and 2012–2022, with early positive trends, particularly in northern WSAF, followed by intensified TWS losses.

## 1. Introduction

This study assesses the contribution of extreme climate Indices on TWS anomaly in Africa semi-arid. 12 temperature and precipitation-based extreme climatic indices are used (Table 1) as defined by the Expert Team on Climate Change Detection and Indices (ETCCDI). The analysis extends beyond mean temperature (TMm) and total precipitation (PRCPTOT) by incorporating additional variables to explore more nuanced and in-depth relationships. Another 10 indices recognized for their importance in water resources have been included in this study (Zhang et al., 2011; Zwiers et al., 2013): percentage of days with temperature above median (TXgt50p); number of low rain days (R1mm); number of heavy rain days (R20mm), Maximum amount of rain that falls in seven consecutive days (Rx7day); warmest daily temperature (TXx); coldest night (TNn); standardized precipitation index for an

accumulation period of 12 months (SPI); standardized precipitation evapotranspiration index (SPEI) for a period of 3, 6 and 12 months.

The ETCCDI was established in 1999, co-sponsored by the World Climate Research Program (WCRP) and Joint WMO/IOC Technical Commission for Oceanography and Marine Meteorology (JCOMM). It developed a globally coordinated set of core climate indices, which includes 27 descriptive indices for moderate extremes (Alexander et al., 2006; Zhang et al., 2011). These indices are valuable for water management, especially in regions vulnerable to the effects of climate change (such as semi-arid areas). They help analyze trends in precipitation and temperature extremes (Agyekum et al., 2022; Rojpratak and Supharatid, 2022; Nugroho et al., 2023). Furthermore, the impact of extreme climate events on TWS will differ depending on the climatic regions (Wunsch et al., 2022; Moghaddasi et al., 2022). Hence, the importance of identifying the climate extremes most representative of

<footnote><sup>a</sup> Geosciences, Water and Environment Laboratory, Faculty of Sciences, Mohammed V University in Rabat, Avenue Ibn Batouta, Rabat, Morocco</footnote>

<footnote><sup>b</sup> Hydrogeology Laboratory, UMR EMMAH, University of Avignon, Avignon, France</footnote>

<footnote><sup>c</sup> UMR G-EAU, IRD, Montpellier, France</footnote>

<footnote><sup>d</sup> International Water Research Institute, College for Sustainable Agriculture and Environmental Science, Mohammed VI Polytechnic University, Benguerir, Morocco</footnote>

<footnote>* Corresponding author. Geosciences, Water and Environment Laboratory, Faculty of Sciences, Mohammed V University in Rabat, Avenue Ibn Batouta, Rabat, Morocco.</footnote>

<footnote>E-mail address: Marc.leblanc@univ-avignon.fr (M. Leblanc).</footnote>
TWS according to the climatic regions of Africa. We use the Long Short-Term Memory (LSTM) neural network to model the variation of TWS, then the feature importance analysis through feature ablation, is applied to assess the contribution score of each input feature. This information is crucial for implementing adaptation and mitigation measures in water management to combat climate change, and to establish early warning systems. The climatic regions considered in our study (Fig. 1) are those used in the Sixth Assessment Report of the IPCC Working Group I (Iturbide et al., 2020).

In this paper, the focus on temperature and precipitation indices is justified by two key factors: first, their role as the primary drivers of the terrestrial water cycle (Rehana et al., 2022; Yin et al., 2023; Ul Shafiq et al., 2024), and second, their critical importance in climate scenario projections (Pérez et al., 2022; Rojpratak and Supharatid, 2022; da Silva et al., 2023). By centring the analysis on these indices, which are fundamental to climate scenario simulations, this study provides a robust framework for forecasting the future dynamics of terrestrial water storage (TWS) under a range of climate change scenarios spanning the next 50–100 years. Studies on total water storage (TWS) have adopted various approaches to assess the impacts of climate change on water resource availability. Most previous research has relied on hydrological models such as WaterGAP and GMDH (Group Method of Data Handling) to simulate TWS variations using climatic parameters (Rateb et al., 2022; Soltani and Azari, 2022; Su et al., 2022). These parameters primarily include precipitation, soil moisture, evapotranspiration, and climate teleconnection indices like ENSO and AMO, which reflect climatic extremes (Anyah et al., 2018; Carvalho Resende et al., 2019; Rateb et al., 2022; Soltani and Azari, 2022; Su et al., 2022; Xie et al., 2022). Precipitation is a primary driver of TWS changes in tropical and subtropical climates, while evapotranspiration plays a crucial role in mid- and high-latitude regions (Zhang et al., 2022; Li et al., 2023). Temperature significantly affects TWS in cold arid and temperate climates (Zhang et al., 2022). The relationships between these climate indices and TWS variations have been investigated using various methodologies, including correlation analyses, principal component analysis, and machine learning methods (Anyah et al., 2018; Carvalho Resende et al., 2019; Kalu et al., 2022; Soltani and Azari, 2022). These approaches have identified significant links between climate indices and TWS fluctuations while highlighting the differentiated impacts of climate teleconnections on water resources. However, it is worth noting that very few studies have examined the connections between the climate extremes defined by ETCCDI and TWS, indicating a critical gap in

understanding these interactions. Furthermore, no previous research has evaluated the contribution of ETCCDI-defined climatic extremes to TWS variability using advanced recurrent neural network methodologies like LSTM. Addressing these gaps, our study focuses on the semi-arid regions of Africa, aiming to provide insights into the role of ETCCDI indices in shaping TWS variability. Total terrestrial water storage (TWS) is a critical component of the global hydrological cycle, influencing water and energy balances while also playing a vital role in weather patterns, geophysical phenomena, and ecosystems (Rodell and Famiglietti, 2001; Sun et al., 2019). Changes in TWS are primarily driven by climate variability, extreme events, and human activities (Scanlon et al., 2022).

In Africa, where regions frequently experience alternating droughts and floods, effective water resource management is crucial (Babaousmail et al., 2022; Scanlon et al., 2022). Recent studies reveal a divergent trend in TWS across the continent, with increases in some areas and significant declines in others, such as North Africa (Rodell et al., 2018; Scanlon et al., 2022). The latest IPCC Working Group II report underscores that Africa is projected to bear the most severe impacts of climate change (Portner et al., 2022). However, most hydrogeological and hydrological studies focus primarily on average future temperature and precipitation changes, often overlooking the implications of extreme climate indices (Gao et al., 2020; Chen et al., 2020; Liu et al., 2021; Yang et al., 2021; Moghaddasi et al., 2022). Projections of extreme indices defined by the ETCCDI suggest significant shifts by the mid- and late-21st century. These include increases in the warmest daily temperature (TXx), the annual coldest night (TNn), and changes in total wet-day precipitation (PRCPTOT) (Ozturk et al., 2021; Babaousmail et al., 2022). Specifically, TNn is expected to rise in the Mediterranean and tropical regions of Africa, while warm nights (TNx) increase and PRCPTOT declines across the Mediterranean, Sahel, West Africa, Central Africa, North-Eastern Africa, and regions within the Middle East and North Africa.

## 2. Data and methods

### 2.1. Study area

Africa is characterized by all major climates, namely: humid climate, sub-humid climate, semi-arid climate, and arid climate (Kottek et al., 2006; Peel et al., 2007; Abiodun et al., 2020; Driouech et al., 2020; Babaousmail et al., 2022). The study area encompasses the four climatic regions based on the distribution in the latest IPCC report (Iturbide et al.,

<table><caption>**Table 1**
List of the 12 extreme indices calculated at monthly time steps using daily data.</caption><thead><tr><th>Indice</th><th>Long Name/Description</th><th>Mathematical description</th><th>unit</th><th>Water Resources and Hydrology (WRH)</th></tr></thead><tbody><tr><td colspan="5"><strong>Extreme Temperature</strong></td></tr><tr><td>1</td><td>TMm</td><td>Mean daily mean temperature</td><td>°C</td><td>Unspecified for a sector</td></tr><tr><td>2</td><td>TXx</td><td>Warmest daily</td><td>°C</td><td>Unspecified for a sector</td></tr><tr><td>3</td><td>TNn</td><td>coldest night</td><td>°C</td><td>Unspecified for a sector</td></tr><tr><td>4</td><td>TXgt50p</td><td>Percentage of days with temperature above the median</td><td>%</td><td>WRH</td></tr><tr><td colspan="5"><strong>Extreme Precipitation</strong></td></tr><tr><td>5</td><td>PRCPTOT</td><td>Total wet-day rainfall</td><td>mm</td><td>WRH</td></tr><tr><td>6</td><td>R1mm</td><td>Number of low rain days</td><td>day</td><td>Unspecified for a sector</td></tr><tr><td>7</td><td>R20mm</td><td>Number of heavy rain days</td><td>mm</td><td>WRH</td></tr><tr><td>8</td><td>rx7day</td><td>Maximum amount of rain that falls in seven consecutive days</td><td>day</td><td>WRH</td></tr><tr><td>9</td><td>SPI (scales of 12 months)</td><td>Standardized Precipitation Index</td><td>mm</td><td>WRH</td></tr><tr><td>10</td><td>SPEI3</td><td>3-month Standardized Precipitation Evapotranspiration Index</td><td>mm</td><td>WRH</td></tr><tr><td>11</td><td>SPEI6</td><td>6-month Standardized Precipitation Evapotranspiration Index</td><td>mm</td><td>WRH</td></tr><tr><td>12</td><td>SPEI12</td><td>12-month Standardized Precipitation Evapotranspiration Index</td><td>mm</td><td>WRH</td></tr></tbody></table>
2020): the northern part of Africa belonging to the Mediterranean zone (MED), the West Southern Africa region (WSAF), the East Southern Africa region (ESAF), and the Madagascar region (MDG) (Fig. 1).

The semi-arid climates across MED, WSAF, ESAF, and MDG exhibit distinct responses to climatic factors due to variations in regional hydrology, precipitation patterns, and temperature trends. In MED, semi-arid conditions arise from declining rainfall influenced by zonal wind patterns and the vulnerability of Mediterranean catchments to drought, with mountain regions playing a crucial role in water balance (Allam et al., 2020; Polo et al., 2020; Djebbar et al., 2020; Derdous et al., 2021; Suárez-Moreno et al., 2022). In contrast, WSAF's semi-arid conditions result from significant year-to-year precipitation variability, the eastward shift of aridity thresholds driven by strengthened westerly airflow, and the influence of El Niño on both surface and groundwater dynamics (Maure et al., 2018; Siderius et al., 2018; Fückar et al., 2020; Dauteuil et al., 2021; Marumbwa et al., 2021; Thomas et al., 2022; Chivangulula et al., 2023). ESAF experiences semi-arid conditions driven by rising temperatures, localized rainfall variability, and increasing desiccation influenced by atmospheric circulation systems (Byakatonda et al., 2018; Caley et al., 2018; Muthoni et al., 2019; Fückar et al., 2020; Jury, 2021; Mupangwa et al., 2023; Demissie and Gebrechorkos, 2024), while Madagascar is classified as semi-arid, particularly in its southern regions, is tied to warming trends, reduced precipitation, and frequent droughts (Andriambeloson et al., 2020; Serele et al., 2020; Barimalala et al., 2021; Carrière et al., 2021; Weiskopf et al., 2021; Randriamarolaza et al., 2022; Rigden et al., 2024). These climatic drivers highlight the role of semi-arid conditions in shaping the terrestrial water storage

(TWS) responses, as limited precipitation, warming trends, and drought exacerbate water scarcity across these regions.

## 2.2. GRACE data: JPL, CSR and GFZ regional solutions

GRACE (Gravity Recovery and Climate Experiment) and its successor, GRACE Follow-On (GRACE-FO), provide the only available satellite observations for measuring global changes in total terrestrial water storage (TWS) (Rodell and Famiglietti, 2001; Bhanja et al., 2016). TWS includes the total storage of surface and subsurface water (Rodell et al., 2009). In our study, we use three GRACE and GRACE-FO regional products often used in previous studies: JPL (Jet Propulsion Laboratory), CSR (Center of Space Research), and GFZ (GeoForschungsZentrum, German Research Centre for Geosciences) (Bhanja et al., 2016; Shiru et al., 2021; Nazari et al., 2022). The average of the three products was used to reduce biases associated with each one. (Nazari et al., 2022; Ouatiki et al., 2022). These products are TWS anomalies, which represent the difference in TWS from the mean TWS over the period 2005–2010. The GRACE data used are RL06 mascon solutions with a resolution of 100 km, covering the period 2002–2021 (PODAAC NASA, National Aeronautics and Space Administration, https://podaac.jpl.nasa.gov). Some data gaps are present in the years 2002, 2003, and from 2011 to 2018.

<figure><img src="image_4.png" /><figcaption>Fig. 1. The spatial repartition of the nine climatic regions of Africa according to the IPCC Sixth Assessment Report (Iturbide et al., 2020).</figcaption></figure>
## 2.3. Climate data

### 2.3.1. Climate prediction center (CPC) data

The Climate Prediction Center (CPC) observational data are a set of near-real-time infrared data merged on a global scale from the approximately 11-µm infrared channels on geostationary satellites GMS-5, GOES-8, GOES-10, Meteosat-7 and Meteosat-5 (Janowiak et al., 2001). The data have been corrected to account for zenith angle dependence to reduce discontinuities between adjacent geostationary satellites (Janowiak et al., 2001). Additionally, CPC data combines observation data from multiple quality-controlled in situ stations (Xie et al., 2007; Fan and van den Dool, 2008; Babaousmail et al., 2022). In this study, we use daily maximum and minimum temperature data, as well as daily precipitation data from CPC, on a 0.5° × 0.5° grid over a 43-year period (1979–2021).

### 2.3.2. Climate indices

The extreme climate indices used are derived from daily minimum and maximum temperatures, as well as daily precipitation from CPC dataset. Nineteen extreme climate indices were selected and classified into four categories: absolute indices, threshold indices, percentile-based threshold indices, and duration indices (Table 1). These indices were computed using the RClimdex package in R (Zhang and Yang, 2004). The last column, "Water Resources and Hydrology (WRH)", indicates whether an index is specifically used in water resources and hydrology studies. Eight of the twelve indices studied were identified as being specifically relevant to water resources and hydrology studies. In contrast, three indices, primarily the temperature indices TMm, TXx, TNn, and the precipitation index R1mm, were not explicitly classified as such.

### 2.3.3. Neural network investigation

The inclusion of multiple highly correlated variables can negatively affect the modelling of the target variable and introduce collinearity, leading to unstable and unreliable results (Bouramtane et al., 2023). To mitigate this issue, it is recommended to calculate scores for the input variables to assess their contribution to the target variable (Sahu et al., 2020). Given the chronological nature of our data, a method capable of capturing the temporal evolution of extreme indices is required to evaluate their influence on TWS variation. Consequently, we employ an LSTM model, a recurrent neural network designed to capture temporal patterns in data (Yu et al., 2019; Okut, 2021). Since the goal of this study is to detect extreme climate events that influence the temporal and spatial variations of TWS, we utilize an LSTM Feature Ablation configuration.

Unlike predictive models, our approach involves sequentially removing extreme indices from the model to evaluate performance based on the absolute mean error and the coefficient of determination. This process, known as feature ablation or feature importance via ablation (Covert and al., 2021), in our case, involves removing one feature at a time, retraining the model, and comparing each test set score against the baseline score. By assessing the impact of each feature removal, we can determine its relative importance in influencing TWS.

Preprocessing of TWS data and climate indices was carefully designed to ensure consistency and quality across the study area. All climate indices were normalized, and datasets were resampled to a uniform spatial resolution of 100 km. The analysis was conducted pixel by pixel, with the temporal series of TWS and the 12 climate indices for each pixel processed separately using the LSTM model. This approach allowed us to account for both spatial heterogeneity and temporal dynamics within each region, ensuring that the identification of the most influential input variable for TWS reflected localized variations. Pixels corresponding to seas or oceans were excluded, leaving 208 pixels for MED region, 240 for WSAF, 252 for ESAF, and 64 for MDG. The LSTM model was configured with a bidirectional layer comprising 512 hidden units, fully connected to generate accurate predictions. The model

utilized the ReLU activation function to capture non-linear relationships, and the Mean Squared Error (MSE) was chosen as the loss function to optimize training. A grid search-based hyperparameter optimization process (GridSearchCV) was conducted to fine-tune key parameters, including the number of units, batch size, learning rate, and window size (e.g., for ESAF: batch size = 16, epochs = 200, units = 512). The LSTM model was trained on 80% of the pixels in each of the four regions (MED, WSAF, ESAF, and MDG) and assessed on the remaining 20% pixels.

The set of mathematical codes governing our LSTM Feature Importance model are as follows:

#### a. Bidirectional LSTM Model

* Forget gate:

$$f_t = \sigma(W_f \cdot [h_{t-1}, x_t] + b_f) \qquad (1)$$

Where $W_f$ and $b_f$ are the weights and biases respectively, $\sigma$ is the sigmoid function, $h_{t-1}$ is the output at the previous time step, and $x_t$ is the current input.

* Input gate:

$$i_t = \sigma(W_i \cdot [h_{t-1}, x_t] + b_i) \qquad (2)$$

$$\tilde{c}_t = \tanh(W_c \cdot [h_{t-1}, x_t] + b_c) \qquad (3)$$

Where $\tilde{c}_t$ is the new candidate memory content, and $\tanh$ is the hyperbolic tangent function.

* Memory update:

$$C_t = f_t \cdot C_{t-1} + i_t \cdot \tilde{c}_t \qquad (4)$$

Where $C_t$ is the memory state at time t, and $C_{t-1}$ is the memory state at the previous time step.

* Output gate:

$$o_t = \sigma(W_o \cdot [h_{t-1}, x_t] + b_o) \qquad (5)$$

$$h_t = o_t \cdot \tanh(C_t) \qquad (6)$$

Where $h_t$ is the output of the LSTM cell.

#### b. Optimization: Adam Optimizer

* Update of the first moment $m_t$

$$m_t = \beta_1 m_{t-1} + (1 - \beta_1) \nabla_{\theta} J(\theta_t) \qquad (7)$$

* Update of the second moment $v_t$

$$v_t = \beta_2 v_{t-1} + (1 - \beta_2) (\nabla_{\theta} J(\theta_t))^2 \qquad (8)$$

* Bias correction

$$\hat{m}_t = \frac{m_t}{1 - \beta_1^t}, \quad \hat{v}_t = \frac{v_t}{1 - \beta_2^t} \qquad (9)$$

* Weight update

$$\theta_{t+1} = \theta_t - \frac{\alpha \hat{m}_t}{\sqrt{\hat{v}_t + \epsilon}} \qquad (10)$$

Where $\alpha$ is the learning rate, $\epsilon$ is a small correction factor, and $\beta_1$ and $\beta_2$
are the momentum parameters.

### c. Cross-Validation Learning

We divide the data into K subsets. For each subset, the model is trained on K-1 subsets and tested on the remaining subset.

### d. Learning rate reduction (ReduceLROnPlateau).

$$ new_{lr} = lr \cdot factor \qquad (11) $$

Where factor is a multiplicative factor when the monitored metric does not improve for a certain number of epochs.

### e. Mean Absolute Error (MAE)

$$ MAE = \frac{1}{n} \sum_{i=1}^{n} |y_i - \hat{y}_i| \qquad (12) $$

Where n is the number of samples, $y_i$ is the true value, and $\hat{y}_i$ is the predicted value.

### f. Coefficient of Determination R²

$$ R^2 = 1 - \frac{\sum_{i=1}^{n} (y_i - \hat{y}_i)^2}{\sum_{i=1}^{n} (y_i - \bar{y})^2} \qquad (13) $$

Where $\bar{y}_i$ is the mean of the true values y.

To evaluate the importance of each feature, we will apply a systematic approach after training each fold of our LSTM model. Specifically, we will analyze the 12 extreme indices to understand fluctuations in Total Water Storage (TWS). For each index, we will generate Out-Of-Fold (OOF) predictions by shuffling its values and observing the impact on the model's performance. If an index is important, the OOF predictions will be less accurate, as reflected by an increase in the Mean Absolute Error (MAE) at that step. This process allows us to assess the significance of each feature.

## 3. Results and discussion

We present the results of the coefficient of determination between extreme indices calculated across four climate regions to explore the relationships between these indices and their association with Terrestrial Water Storage (TWS) anomalies in predominantly semi-arid areas. Additionally, we assess the importance of each climate extreme index in influencing TWS variability using LSTM to get the Feature Importance via ablation, revealing the respective contributions within these regions. Finally, we analyze the temporal evolution, correlation, and spatial trends of both TWS and the extreme indices that significantly impact its variation, based on the four climate regions defined by the IPCC across Africa.

### 3.1. Coefficient of determination: extreme indices and TWS

Fig. 2 presents the results of the determination coefficient (R²) analysis between TWS and the 12 extreme temperature and precipitation indices across the four regions (MED, WSAF, ESAF et MDG). The result shows that there is a low dependence (R² ≤ 0.5) between TWS and these indices in the four semi-arid regions. This weak correlation may be due to the potential non-linear relationship between climate extremes and GRACE Terrestrial Water Storage (TWS) (Anyah et al., 2018).

While it is well established that climate particularly extreme temperature and precipitation events significantly influences water storage (Khandu et al., 2016; Zheng et al., 2023), the relationship between

<figure><img src="image_5.png" /><figcaption>Fig. 2. Coefficient of determination for the spatial average between TWS and the 12 monthly extreme indices over four climate region Africa from 2002 to 2021.</figcaption></figure>

temperature and precipitation indices and TWS may be more complex than linear models can capture (Kassem et al., 2021; Oukawa et al., 2022; Bonsoms and Ninyerola, 2024). In semi-arid and arid regions, climatic extremes impact various hydrological processes, such as surface water flow (Gudmundsson et al., 2021; Wang et al., 2024) and groundwater recharge (Nanekely et al., 2023; Navarro Céspedes et al., 2024), which in turn affect TWS in a non-linear manner. For example, in regions with extensive surface water irrigation, the water applied for irrigation infiltrates the soil and recharges groundwater, illustrating a non-linear interaction where surface water flow indirectly influences groundwater storage and TWS (Döll et al., 2012). Conversely, as indicated by Meghwal et al. (2019), groundwater storage, as revealed through GRACE data, is closely linked to groundwater pumping and policies aimed at reducing groundwater abstraction for irrigation, particularly during periods of drought. These factors suggest that changes in groundwater storage due to human activities and policies, especially in dry conditions, may not follow a simple linear pattern.

Therefore, to better capture these complex interactions, it is essential to use advanced non-linear methods, such as neural networks, which can more accurately represent the non-linear relationships between climatic indices and TWS (Kassem et al., 2021; Oukawa et al., 2022; Bonsoms and Ninyerola, 2024).

### 3.2. Best indices: LSTM feature importance through ablation

The LSTM Feature Importance analysis through ablation method for the four semi-arid regions demonstrates high predictive accuracy on the test set, with R² scores of 0.80, 0.89, 0.92, and 0.73 for the MED, WSAF, ESAF, and MDG regions, respectively, showcasing the model's robust capability to capture TWS across different regions (Fig. 3). The results consistently highlight the SPEI Index as the most influential predictor of TWS across various time scales, particularly 6, and 12 months, in all regions. In the MED region, SPEI12 is the dominant factor, contributing to a Mean Absolute Error (MAE) of 0.05 m (Fig. 3a). In WSAF, ESAF, and MDG, SPEI6 has the greatest impact on TWS variability, with MAE values of 0.016 m, 0.017 m, and 0.020 m, respectively (Fig. 3b-d). For all four regions, the second-best climatic indices are also represented by different SPEI time scales, distinct from the primary predictors.
<figure><img src="image_6.png" /></figure>

<figure><img src="image_7.png" /></figure>

<figure><img src="image_8.png" /></figure>

<figure><img src="image_9.png" /><figcaption>Fig. 3. The distribution of the contribution in descending order of extreme climate indices by the LSTM Feature Importance analysis via ablation method in the regions: (a) MED, (b) WSAF, (c) ESAF, and (d) MDG.</figcaption></figure>

Specifically, SPEI6 ranks second in the MED region, SPEI3 in both WSAF and ESAF regions, and SPEI12 in the MDG region.

The indices that depend solely on precipitation and temperature did not exhibit significant importance for TWS variation across the four study areas. Additionally, the baseline model, which aggregates all indices except for the one being evaluated, showed low significance in terms of its contribution to TWS fluctuations in all four regions. In contrast, SPEI demonstrated the strongest relevance for TWS, highlighting its capacity to capture the impact of drought conditions on water storage. This can be attributed to the fact that SPEI represents the hydrological balance, as it is defined by the difference between precipitation (P) and evapotranspiration (ET). By incorporating both water inputs and atmospheric water losses, SPEI provides a more comprehensive measure of water availability compared to indices that consider precipitation alone. This enables SPEI to better reflect variations in total water storage, particularly in semi-arid environments where

evapotranspiration plays a crucial role in modulating groundwater recharge and surface water availability.

Drought conditions, characterized by an imbalance between precipitation and evapotranspiration, directly affect water availability across multiple hydrological components, including surface water, groundwater, and soil moisture (Vicente-Serrano et al., 2020; Choudhury et al., 2021; Hernández-López et al., 2024). During drought periods, particularly in semi-arid areas, reduced precipitation and increased evapotranspiration lead to decreased soil moisture, reduced surface runoff, and ultimately groundwater depletion (Weiskopf et al., 2021; Henao Casas et al., 2022; Nenweli et al., 2024). This cascading effect across different water components makes SPEI well-suited to capturing variations in TWS in semi-arid areas (Zhang et al., 2018). As drought progresses, precipitation deficits propagate through the hydrological system, impacting various TWS components with varying response times depending on the drought's severity and duration (Van
Loon, 2015). SPEI, accounting for both precipitation and evapotranspiration, captures this sequential depletion of water resources across different timescales, allowing it to effectively predict changes in TWS.

The localized impact of SPEI on TWS, with SPEI12 in the MED region, SPEI6 in WSAF, ESAF and MDG, suggests that each semi-arid region's hydrological dynamics respond differently to drought indices based on their climatic and geological conditions. This variation aligns with the concept that different regions and hydrological subsystems respond to droughts at varying timescales (Vicente-Serrano et al., 2010; Van Loon, 2015; Zhang et al., 2018). Longer timescales, such as SPEI12, are better suited for capturing groundwater variations and prolonged hydrological droughts (Zhang et al., 2018), as observed in the MED region where significant groundwater depletion has occurred. This is largely due to the recurring, prolonged, and intense drought periods over the last 50 years, which have drastically reduced water availability and increased reliance on groundwater resources (Tanarhte et al., 2024). The sustained nature of these droughts makes SPEI12 particularly effective in capturing the cumulative impacts on TWS in the region (Zhang et al., 2018).

SPEI6, representing a six-month timescale, offers a comprehensive view of hydrological droughts by balancing short-term fluctuations with longer-term hydrological cycles (Asong et al., 2018; Gebremeskel et al., 2020). It captures both wet and dry periods, making it especially useful for understanding transitions between meteorological and hydrological droughts. This characteristic makes SPEI6 a particularly suitable input feature for assessing TWS in both Madagascar (MDG) and the West Southern-Africa (WSAF) and the East Southern-Africa (ESAF). In MDG, SPEI6 is crucial as it reflects the cumulative effects of precipitation deficits and evapotranspiration during the period when the country receives up to 60% of its annual rainfall between December and March. Any reduction during these months can significantly affect the recharge and depletion of surface water and groundwater (Guhathakurta and Wagh, 2022). Similarly, in WSAF and ESAF, SPEI6 is highly effective for assessing TWS, particularly in regions with a Mediterranean-type climate, where most rainfall occurs during the winter months (June to August) (Botai et al., 2017; Odoulami et al., 2021). SPEI6's longer timescale makes it ideal for reflecting both short-term meteorological changes and the cumulative effects of long-term hydrological cycles, influenced by large-scale climatic drivers such as the Antarctic Oscillation (AAO) and El Niño-Southern Oscillation (ENSO) (Philippon et al., 2012; Weldon and Reason, 2014).

### 3.3. Temporal evolution: TWS and the most influential climate indices

Fig. 4 presents the temporal variation graphs of TWS and the most important climate index, as determined by the LSTM model, for the four study regions, normalized over the entire TWS data period, with trend lines for the first and second decades. It is important to note that the anomalous variations observed between 2018 and 2019 across the four graphs are attributed to the absence of TWS data during this period.

In both the MED and MDG regions, fluctuations in TWS closely align with drought indices, and their trend lines exhibit a strong relationship, displaying nearly overlapping negative trends over time (Fig. 4). In the MED region, TWS shows a strong correlation with the SPEI12 index, with both variables following similar patterns of abrupt changes and a general negative trend from 2002 to 2022. This continuous decline suggests long-term reductions in surface water and groundwater storage exacerbated by prolonged drought episodes (Zhang et al., 2018; Vicente-Serrano et al., 2020). In the MDG region, the temporal relationship between TWS and SPEI6 exhibits a consistent downward trend throughout the study period. From 2002 to 2012, both TWS and SPEI6 showed a pronounced decline, and this trend persisted from 2012 to 2022 with nearly overlapping negative trajectories. The strong alignment between these trends suggests a direct response of TWS to prolonged drought conditions, reinforcing the dominance of climatic drivers over total water storage dynamics. (Asong et al., 2018; Gebremeskel et al., 2020).

In the WSAF region, the temporal variation of TWS closely follows that of the SPEI6 drought index, with two distinct phases emerging. From 2002 to 2011, both TWS and SPEI6 exhibit an upward trend, reflecting wetter conditions and reduced drought stress, which likely supported Terrestrial Water Storage across the region. However, starting in 2012, both variables shift towards a sustained downward trend, continuing through 2022, indicating intensifying drought severity and a significant reduction in total water availability (Botai et al., 2017; Guhathakurta and Wagh, 2022). Despite the decline after 2012, the overall trend for TWS from 2002 to 2020 reveals a net increase, in contrast to the general decline seen in SPEI6. This divergence suggests that while TWS fluctuates with drought conditions, as captured by SPEI6, other factors such as occasional higher recharge years may have contributed to maintaining a longer-term increase in Terrestrial Water Storage (Philippon et al., 2012; Weldon and Reason, 2014; Herrnegger et al., 2021; Byrne et al., 2024).

In the ESAF region, TWS and SPEI6 exhibit closely aligned fluctuations, both showing a downward trend from 2002 to 2012. However, from 2012 onward, while their patterns remain similar, neither variable shows a significant trend, except for a slight negative trend in TWS, with a noticeable time lag developing between the two. For instance, towards the end of 2009, an increase in SPEI6 does not translate into a corresponding rise in TWS until mid-2010. This phase shift persists until 2014, during which TWS continues to decline, whereas SPEI6 demonstrates a rising trend. From 2016 to 2022, both TWS and SPEI6 show similar fluctuations, still with a slight lag. Overall, between 2002 and 2022, TWS and SPEI6 demonstrate a consistent downward trajectory, indicating long-term water stress in the region (Edossa et al., 2014; Gebremeskel et al., 2020).

### 3.4. Variability and spatial trend: TWS and the extreme indices

Figs. 5–8 present the spatial distribution trend maps over the periods 2002–2011 and 2012–2022, while Fig. 9 shows the correlation maps of TWS trends with the most influential climate indices for the four study regions (MED, WSAF, ESAF, and MDG). The significance of these trends was assessed using the Mann-Kendall test, and the slopes were computed using Sen's method at a 0.05 significance level.

In the Mediterranean region, TWS and SPEI12 exhibit similar spatial distributions across both decades, reinforcing the strong link between drought intensity and water storage. During the first decade (2002–2012), the western Mediterranean (west of the 0° meridian) shows a positive trend, with TWS increasing by 2–6 mm per years and SPEI12 ranging from 0.2 to 0.3 per years. In contrast, the eastern Mediterranean (east of the 0° meridian) displays negative trends, with TWS declining by –6 mm to –18 mm per years and SPEI12 decreasing between –0.5 and –0.4 per years. However, this spatial pattern reverses in the second decade (2012–2022), where the western Mediterranean shifts to a negative trend and the eastern Mediterranean to a positive trend.

This shift underscores the region's transition toward increasingly severe drought conditions, resulting in excessive declines in TWS. The persistent negative trends reflect intensifying water stress, exacerbating groundwater depletion and surface water deficits, which pose significant challenges for water resource management and ecosystem sustainability. The spatial correlation patterns between TWS and SPEI12 further highlight this relationship, particularly in the Maghreb region (between 20°W and 20°E), where correlation values range from 0.3 to 0.7, indicating a strong connection between drought intensity and water storage. This correlation is especially critical in groundwater-dependent areas of the Maghreb region, where reduced precipitation and prolonged droughts have directly impacted water availability (Weiskopf et al., 2021; Henao Casas et al., 2022; Nenweli et al., 2024). East of 20°E, the correlation weakens, with some localized negative correlations, suggesting more complex hydroclimatic interactions.
<figure><img src="image_10.png" /><figcaption>**Fig. 4.** Monthly Temporal Variation in Mean GRACE (JPL, CSR, GFZ) and Dominant Extreme Indices Across Africa's 4 Climatic Regions (IPCC6) from 2002 to 2021: (a) MED, (b) WSAF, (c) ESAF and (d) MDG. The significance of these trends was assessed using the Mann-Kendall test, and the slopes were computed using Theil-Sen's method at a 0.05 significance level.</figcaption></figure>
<figure><img src="image_11.png" /><figcaption>Fig. 5. The spatial trend map for 2002–2011 (a, b) and 2012–2021 (c, d) for: TWS (a, c) and SPEI12 (b, d) in MED. Squares indicate significant trends. The significance of these trends was assessed using the Mann-Kendall test, and the slopes were computed using Theil-Sen's method at a 0.05 significance level.</figcaption></figure>

The combined spatial analysis of trends and correlations between TWS and SPEI12, provides a comprehensive view of the complex dynamics of water stress in the Mediterranean region. The strong correlation between TWS and SPEI12 suggests that drought conditions play a crucial role in driving changes in water storage. This finding aligns with the broader understanding that prolonged droughts significantly affect groundwater recharge and surface water availability (Zhang et al., 2018; Vicente-Serrano et al., 2020). In areas such as the northeastern Mediterranean, where slight positive trends in TWS and SPEI12 are observed, along with weaker correlations, localized factors may be mitigating broader drought impacts (Fig. 5c and d). These areas may offer important insights into resilience to drought and climate variability, although their marginal improvements are overshadowed by the overall negative trends across most of the region.

In both the WSAF and ESAF regions (Figs. 6 and 7), TWS and SPEI6 exhibit similar spatial distribution patterns, characterized by a distinct reversal of trends between the two decades (2002–2011 and 2012–2022). During the first period (2002–2011), TWS trends show a general positive trend across the WSAF region, with values ranging from 12 mm per years to over 24 mm per years (Fig. 6a). These increases are particularly concentrated inland, north of the region, where TWS trends exceed 16 mm per years, aligned in a northeast-southwest orientation. SPEI6 also exhibits a positive trend during this period, ranging from 0.15 to 0.3 per years, particularly in the northeastern part of WSAF, where TWS trends are highest (Fig. 6b). Other areas in the south also display positive SPEI6 trends, while highly localized zones in the south and along the northeastern coast exhibit negative SPEI6 trends. In the ESAF region, the positive TWS trends observed during 2002–2011 form a continuation of the pattern seen in eastern WSAF. However, these trends are confined to areas where SPEI6 also shows positive values (Fig. 6a).

In the second period (2012–2022), a sharp inversion occurs in both regions, with TWS and SPEI6 trends shifting from positive to negative. This reversal is particularly pronounced in the northern part of WSAF, where the negative TWS trend is more significant compared to the south. SPEI6 also exhibits substantial negative trends, ranging from –0.15 to –0.3 per years, concentrated in both the northern and southern parts of WSAF, while the central area shows a positive trend. Similarly, in ESAF, negative TWS trends dominate almost the entire region, except for localized positive trends near the northern boundary. During this period, SPEI6 trends also become predominantly negative, with stronger declines in the northern and southern parts, while positive trends become

increasingly localized.

The correlation patterns between TWS and SPEI6 exhibit similar spatial distributions in both WSAF and ESAF, emphasizing strong regional contrasts (Fig. 9). In both regions, the southern and central areas show very high positive correlations, ranging from 0.4 to 0.6 (Fig. 6b1), indicating that variations in water storage are closely linked to changes in drought intensity. This suggests that in these areas, wetter conditions (as indicated by positive SPEI6 trends) contribute significantly to TWS increases, while prolonged droughts lead to substantial declines in water storage (Zhang et al., 2018; Xu et al., 2021). Conversely, the northern parts of WSAF and ESAF exhibit negative to weak correlations between TWS and SPEI6, ranging from –0.3 to –0.6. This pattern is likely due to the shift in trends over time as observed in Figs. 6 and 7. During the 2002–2011 period, both TWS and SPEI6 showed positive trends in the northern regions. However, in the following decade (2012–2022), these trends reversed to negative, which likely influenced the observed correlation patterns.

Overall, The hydroclimatic signal in the WSAF region is quite complex and largely driven by two antagonist extreme events: a drought in the south and a wet period in the north of the region. Liu and Zhou (2021) highlighted that Namibia experienced an increasing trend in precipitation between 2001 and 2010, followed by a significant decline from 2011 to 2020. This wet event extends beyond the WSAF region all the way to the Rift Valley where it has been the strongest (Herrnegger et al., 2021; Byrne et al., 2024). Fig. 7 shows that a small part of the ESAF region, in the northwest, is also affected by this wet period. On the other hand, drought conditions have been prevailing in the south of the WSAF and ESAF regions since 2015 (Mahlalela et al., 2020; Chikoore and Jury, 2021; Meza et al., 2021).

During the period 2002–2011, the entire MDG region exhibited a general negative trend in both TWS and SPEI6. TWS trends ranged between –6 mm per years and –12 mm/5 years, indicating a substantial decline in water storage (Fig. 8a). Similarly, SPEI6 trends showed significant negative values, ranging from –0.3 to –0.4 per years (Fig. 8b), highlighting the intensification of drought conditions across the region (Bigot et al., 2021; Weiskopf et al., 2021). This pattern aligns with broader drought occurrences in Madagascar, particularly affecting the central highlands and the southern region, where droughts have been recurrent and severe (Rakotoarison et al., 2018). The increase in drought frequency, particularly in southern Madagascar (UNICEF and WHO, 2019), has likely contributed to these observed reductions in water
<figure><img src="image_12.png" /><figcaption>**Fig. 6.** The spatial trend map over 5-year periods for 2002–2011 (a, b) and 2012–2021 (c, d) for: TWS (a, c) and SPEI6 (b, d) in WSAF. Squares indicate significant trends. The significance of these trends was assessed using the Mann-Kendall test, and the slopes were computed using Theil-Sen's method at a 0.05 significance level.</figcaption></figure>

storage. In the subsequent period (2012–2022), the trends shifted spatially. In the northern part of the region, emerging positive trends were observed for both TWS and SPEI6, indicating a partial recovery in water storage and drought conditions (Fig. 8c and d). However, in the

southern region, the negative trends intensified, with TWS trends exceeding –12 mm per year and SPEI6 trends reaching –0.6 per year. This sharp contrast suggests an increasing severity of drought in the south, further stressing water resources, while the north appears to
<figure><img src="image_13.png" /><figcaption>**Fig. 7.** The spatial trend map for 2002–2011 (a, b) and 2012–2021 (c, d) for: TWS (a, c) and SPEI6 (b, d) in ESAF. Squares indicate significant trends. The significance of these trends was assessed using the Mann-Kendall test, and the slopes were computed using Theil-Sen's method at a 0.05 significance level.</figcaption></figure>

benefit from more favourable climatic conditions attributed to the
tropical climate and monsoon-driven rainfall, which provide substantial
precipitation during the summer months (Mavume et al., 2010; Rako-
tarison et al., 2018). The correlations between TWS and SPEI6 reveal
general positive relationships (ranging from 0.3 to 0.6) all over MDG
region, indicating a direct relationship between water storage and

drought intensity (Fig. 9 d).

From all the above, it is clear that hydrogeological parameters play a
crucial role in the variation of TWS. This may well contribute to explain
why SPEI accumulation period vary from one semiarid region to
another. This study focused on temperature and precipitation-based
indices as these are the two main forcing variables of the water cycle.
<figure><img src="image_14.png" /><figcaption>**Fig. 8.** The spatial trend map for 2002–2011 (a, b) and 2012–2021 (c, d) for: TWS (a, c) and SPEI6 (b, d) in MDG. Squares indicate significant trends.</figcaption></figure>

In future studies, it would be interesting to include other factors impacting the land surface such as land use, irrigation practices and population density (e.g. Xu et al., 2019). Additionally, in the MED region agricultural water use, and especially groundwater extraction for irrigation, may contribute to observed TWS trends (Frappart, 2020; Ouatiki

et al., 2022). Khandu et al. (2016) also noted that in areas where a decoupling between TWS and drought is noted groundwater abstraction is often driving TWS. Moreover, to further improve the forecasting of TWS, it would be beneficial to account climatic teleconnection phenomena (such as ENSO and NAO, North Atlantic Oscillation) with TWS
<figure><img src="image_15.png" /><figcaption>**Fig. 9.** The map of correlation of TWS and the climate index with important impact on the temporal variation of TWS in the four regions: MED (a), WSAF (b), ESAF (c), and MDG (d).</figcaption></figure>

variability (Carvalho Resende et al., 2019).

## 4. Conclusion

In this study, we analyzed Terrestrial Water Storage (TWS) and its relationship with 12 climate extreme indices across four semi-arid regions in Africa: the Mediterranean (MED), West Southern Africa (WSAF), East Southern Africa (ESAF), and Madagascar (MDG). Using TWS data from JPL, CSR, and GFZ, and extreme climate indices from CPC data, we employed LSTM recurrent neural networks to assess the importance of each climate index and performed temporal and spatial trend analyses to investigate the climatic, hydrological, and hydrogeological processes driving TWS variation.

Our findings revealed strong predictive capabilities of the LSTM model, with R² values ranging from 0.73 to 0.91. The Standardized Precipitation Evapotranspiration Index (SPEI) emerged as the most significant predictor for TWS across all four regions. Specifically, SPEI12 was the dominant factor in the Mediterranean, while SPEI6 was crucial for WSAF, ESAF, and MDG. Regional trends highlighted that TWS in the

MED and MDG regions showed long-term declines, aligning with intensified droughts (2002–2022). In WSAF and ESAF, TWS trends reversed between the two decades, with positive trends observed from 2002 to 2011, followed by negative trends and intensified TWS losses from 2012 to 2022, particularly in northern WSAF. This variability underscores the importance of local hydrological processes and short-term drought impacts, with mixed trends observed in ESAF, where the northwest saw increases while the central areas experienced sharp declines. The correlation between TWS and SPEI6 showed strong positive relationships (0.4–0.6) in central and southern WSAF, as well as in ESAF, indicating that water storage is closely linked to drought intensity in these areas. In contrast, negative correlations (–0.3 to –0.6) were observed in northern WSAF, reflecting a shift in hydroclimatic trends in that region.

Finally, Incorporating the appropriate SPEI timescales (SPEI12 and SPEI6) into hydrological models has proven valuable in enhancing the simulation of water resource availability, as it accounts for the region-specific hydrological processes and climate extremes. These results provide critical insights into groundwater recharge, the intensification
of evapotranspiration, and overall water demand and retention. Such insights are crucial for improving water resource management in Africa's semi-arid regions. By considering the different timescales and their impact on TWS dynamics, the study offers a more robust framework for tackling challenges posed by climate extremes, especially in the context of drought and shifting water availability.

### CRediT authorship contribution statement

**Komi Mawulom Claude Afamondji:** Writing – review & editing, Writing – original draft, Visualization, Validation, Supervision, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. **Ismail Mohsine:** Visualization, Validation, Software, Methodology, Investigation, Data curation, Conceptualization. **Nourelhouda Karmouda:** Visualization, Validation, Methodology, Investigation, Data curation, Conceptualization. **Tarik Bouramtane:** Writing – review & editing, Writing – original draft, Visualization, Validation, Supervision, Methodology, Investigation, Formal analysis, Conceptualization. **Mounia Tahiri:** Visualization, Supervision, Project administration, Funding acquisition, Conceptualization. **Ilias Kacimi:** Visualization, Validation, Supervision, Project administration, Methodology, Funding acquisition, Formal analysis, Conceptualization. **Sarah Tweed:** Writing – review & editing, Writing – original draft, Visualization, Project administration, Funding acquisition, Data curation, Conceptualization. **Fatima Driouech:** Writing – review & editing, Writing – original draft, Visualization, Validation, Supervision, Conceptualization. **Marc Leblanc:** Writing – review & editing, Writing – original draft, Visualization, Validation, Supervision, Software, Resources, Project administration, Methodology, Investigation, Funding acquisition, Formal analysis, Data curation, Conceptualization.

### Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

### Acknowledgment

This work was supported by the River Basin Agency of Bouregreg and Chaouia, the River Basin Agency of Loukkos, and the Research and Water Planning Directorate of Morocco. It was funded by the Institut de Recherche pour le Développement (IRD) through the JEAI PESAS project. The research was conducted at the Laboratory of Geosciences, Water, and Environment, Faculty of Sciences, Mohamed V University of Rabat, Morocco.

### Data availability

Data will be made available on request.

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