Lahore, a UNESCO city in Pakistan, is projected to rise from the sixth to the third fastest-growing city worldwide by 2030. This rapid urbanization challenges its commitment to cultural and ecological preservation, positioning it as an international case study in urbanization research. Thus, the Lahore Development Authority emphasizes the need for ongoing monitoring of urban dynamics to support effective planning and achieve sustainability targets, including Sustainable Development Goal 11. To contribute, we used high-resolution Landsat imagery to analyze the spatial diverging patterns of urban extent from 1998 to 2023 in Lahore. Additionally, we employed a Cellular Automata (CA) Markov Chain model to project urban growth over the next 25 years. As of 2023, we estimated that approximately 53.6% (92,660.9 ha) of Lahore is urbanized, while 46.4% remains unaffected by urban activities. Projections for 2048 suggest that the urban footprint may expand to 75.8% (131,031.5 ha), leaving only 24.2% of the area free from urbanization. Our analysis also revealed divergent urban expansion patterns significantly impacting local ecosystems. It showed a 31% reduction in inland water bodies, a 39.8% loss of vegetation, and a 60.1% decrease in sparse areas, all attributable to urban development. As natural landscapes are replaced by built environments, Lahore is likely facing increasing challenges that could jeopardize the city's green growth and urban ecological balance. Therefore, we urge land use managers, urban planners, and stakeholders in Pakistan to promote initiatives that enhance urban resilience, particularly through smart city planning and creating green and blue spaces. By focusing on Lahore, this study also provides valuable insights that can serve as a benchmark for other rapidly urbanizing cities facing similar challenges.

Lake Issyk-Kul is an endorheic lake in arid Central Asia that is vital to the region's ecological sustainability and socio-economic development. Climate change and anthropogenic water consumption led to fluctuations in the lake's water level, which affected the water resource. The goal of this study was to examine the impacts of climate change and human activities on the Issyk-Kul water balance by combining the Coupled Model Intercomparison Project Phase 6 (CMIP6) scenarios with hydrological modeling. The Soil and Water Assessment Tool (SWAT) model was used to incorporate signals of future precipitation and temperature changes. According to the scenarios, the total discharge of the three catchments showed an overall increasing trend with a maximum value of 28.02%. The snow and ice-melt water from March to August was revealed, and the increasing trends only occurred from March to May, with the snow and ice melting peak variations ranging from 0.5% to 2%. The high increase in change appeared in northern catchment of the lake. There was an exceptional upward precipitation trend over the northern catchment, with annual increases ranging from 0.7 to 14.5%, and an average annual temperature of 1.72°C. With slight similarities, the total runoff would increase for all catchments, with an average annual value of 10.6%. The northern catchment was significantly more sensitive to precipitation and warming than the southeastern catchments. Under land use land cover change, average annual discharge decreased with agricultural expansion, with discharge differences ranging from −0.005 to −1.06 m3/s. The findings are useful for decision-makers addressing the challenges of climate change mitigation and local water resource management.

Introduction and aimEnsuring the protection and restoration of forest ecosystems is vital to maintaining and restoring ecological balance in deforested or degraded landscapes. However, sustainable development faces challenges from high human impacts on natural forest ecosystems, insufficient advanced conservation measures, and limited engagement of local communities in developing nations. The aim of this study was to explore the utility of spatial remote sensing datasets in examining the landscape pattern changes within the transboundary Nyungwe-Kibira Forest from 2000 to 2019. This aimed to emphasize the necessity of understanding the intricate dynamics of this ecosystem and its susceptibility to human activities in order to bolster diverse restoration initiatives throughout the region.MethodsThe landscape pattern change in the Nyungwe-Kibira between 2000 and 2019 was analysed using high-resolution Landsat data. This analysis encompassed an evaluation of the dynamics of changes in built-up, cropland, and forest areas within the region. Especially, primary data derived from the Landsat dataset and secondary data from reports such as the Outlook Report were employed to elucidate the ongoing landscape transformation within and surrounding the transboundary forest.Results and discussionThe analysis revealed a net change of +62.3% and +18.07% in built-up and cropland areas, resulting in a gross change of 14,133 ha and 6,322 ha in built-up and cropland areas, respectively. Furthermore, the forest experienced an overall gain of 9.11%, corresponding to a net loss of 6.92% due to deforestation, estimated at -14,764 ha. The analysis also indicated that built-up areas accounted for approximately 33.02% of the net forest loss, primarily affecting the northern edge of the Rwanda region, while cropland expansion contributed to a net loss of forest (-9.48%), predominantly impacting the southeast portion of the forest in Burundi. Additionally, the forest is predicted to decrease by 0.74% by 2030, with current findings showing aggregated forest and cropland at 66% and 7%, dissected rangeland at 24%, and created built-up areas at 3%. The findings indicate that the Nyungwe-Kibira Forest is undergoing notable transformations, highlighting the necessity of land-based projects and mitigation plans to facilitate the restoration of the forest from its historical changes. Without proactive measures, an ongoing decrease in forest area by 2030 is anticipated.

The growing resource constraints around land, water, and energy while tackling climate change in Central Asia threaten the agricultural sustainability that underpins food security and people livelihoods. However, there is a lack of comprehensive long-term evaluations of the intricate water -food -energy -carbon nexus on a wider scale, hindering sustainable agriculture across this region. This study developed an integrated top -down and bottom -up approach to quantify the water consumption, energy use, and carbon emissions from crop production in Central Asia and established the coupling coordination model to evaluate the nexus (1995 -2020). The results show that the total water consumption in Central Asia increased from 161.55 x 10 9 to 170.93 x 10 9 m 3 at a rate of 1.39 x 10 9 m 3 / a in which blue water accounted for 59%. However, the energy use and carbon emissions decreased at a rate of - 1.57 PJ/a and - 0.06 Mt CO 2 eq /a, respectively, in which the indirect energy and indirect carbon emissions varied greatly. Furthermore, the coupling coordination level of water -food -energy -carbon nexus oscillated between uncoordinated and transitional development stages before 2000 and then entered coordinated development in 2003 after a short transitional development. The findings revealed that this oscillation may have been abnormal owing to issues such as yield loss and soil degradation. These results highlight the complex interplay between water, food, energy, and carbon of crop production across Central Asia and emphasise the need for a more integrated policymaking with increased regional cooperation, improvement in resource utilization efficiency, agricultural structure optimization, and protection of farmers rights towards agricultural sustainability.

The Nyabarongo basin in Rwanda is subjected to hydrometeorological hazards, particularly floods, which are the most prevailing and devastating. Therefore, understanding flood-controlling factors is so pertinent for the development of scientifically driven flood prevention strategies. This study aimed at exploring a form of pixel-based information value model integrated with remote sensing techniques and geo-information system to assess the probability of flood incidence and geo-visualize prone areas at basin’s scale. To do this, a flood inventory was initially generated using 226 past flooded locations, which were split into a 75:25 ratio for model training and validation, respectively. Fourteen flood-controlling factors were selected after a multicollinearity diagnosis. The results unveiled that more than half of the basin’s surface area is covered by very high (8.6

The paucity of in-situ records, particularly in the glaciated mountainous region, is an obstacle in cryosphere ecology and environmental studies. Generally, available gauge station data is fragmented and covers valleys; thus, the use of gridded precipitation products (GPPs) is crucial in such complex terrains. However, these GPPs suffer from systematic biases and uncertainties owing to parameterization deficiencies. Therefore, the main goal of this research is to systematically evaluate the long-term performance and differences of the newly launched MSWEP in comparison to APHRO, CHIRPS, ERA-5, and PGMFD over the transboundary region of Indo-Pak (1981–2009) at spatial (whole to sub-basins) and temporal (daily to seasonal) scales. Findings reveal (1) overall, five GPPs produced well annual spatial precipitation variability with high magnitudes in the northwestern and low in the northeastern region. (2) The estimations from GPPs also divulged better correlation with in-situ observations (MSWEP = 0.86, APHRO = 0.76, ERA-5 = 0.81, CHIRPS = 0.57 and PGMFD = 0.68) at daily span. Better performance was observed during the monsoon compared to winter and pre-monsoon seasons. (3) Lately, estimates from MSWEP are more reliable for all the seasons, especially in the winter season, with the highest CC (0.90) and lowest relative bias (3.03%). (4) All GPPs (excluding ERA-5) overestimated light precipitation (0–1 mm/day) and underestimated moderate to heavy precipitation, in contrast to the ERA-5 that tended to underestimate the light but overestimate moderate (1–20 mm/day) and heavy precipitation (>20 mm/day) events. The CHIRPS was less accurate in detecting most of the precipitation events. The MSWEP product captured all precipitation intensities more accurately than other GPPs. The current research indicates considerable implications for product improvement and data users for choosing better alternative precipitation data at a local scale.

The current study evaluates consistency among three Normalized Difference Vegetation Index (NDVI) datasets, namely GIMMS, MODIS and SPOT, to characterize alpine vegetation dynamics (greening and browning) across High Mountain Asia (HMA) in 2001-2015. The utility of these datasets is explored to evaluate the vegetation's variability at different spatial-temporal scales and, elevation, and to compare their spatial trends and distribution patterns. In addition to the Pearson correlation coefficients performed to quantitatively analyze the consistency and inconsistency of each dataset, an NDVI quality control (QC) layer and Landsat NDVI are also used to evaluate the findings. The results indicate that the GIMMS has the highest NDVI mean, while SPOT has the lowest. However, GIMMS also showed a browning trend for both Tianshan (TS) and the Qinghai Tibet Plateau (TP) at a rate of -0.3 x 10(-3) per year, whereas MODIS and SPOT exhibit a greening trend (TSMODIS = 0.5 x 10(-3) per year, TSSPOT = 0.6 x 10(-3) per year, TPMODIS = 0.9 x 10(-3) per year, TPSPOT = 1.6 x 10(-3) per year). Furthermore, MODIS-SPOT shows the highest correlation (R-GREEN = 0.73; R-BROWN = 0.47), followed by MODIS-GIMMS, and GIMMS-SPOT. The overall, NDVI trend consistency appears to be higher in TS. Finally, the consistent greening pixels mainly distributed in central TP stretching to the northeastern part, and in western stretching to eastern TS, account for 32.14%, while 8.32% of consistent browning pixels are concentrated in southwestern TP and central TS. The inconsistent pixels account for 59.54%, with 39.21% of inconsistent greening pixels being widely distributed across HMA, and 20.58% of inconsistent browning pixels being relatively pronounced in central TS and southern TP. This study provides baseline inferences for the selection and reconstruction of data in follow-up studies on vegetation dynamics.

The increasing demand of water results in the overexploitation of water resources. This situation calls for more effective water management alternatives including rainwater harvesting (RWH) systems. Due to the lack of biophysical data and infrastructure, the identification of suitable sites for various RWH systems is a challenging issue. However, integrating geospatial analysis and modeling approaches has become a promising tool to identify suitable sites for RWH. Thus, this study aimed at identifying suitable sites for RWH in the Nyabugogo catchment located in Rwanda by integrating a geo-information-based multi-criteria decision-making (MCDM) and SWAT (Soil and Water Assessment Tool) model. Moreover, the sediment yield was compared to the soil erosion evaluated using the Revised Universal Soil Loss Equation (RUSLE) owing to the lack of sediment concentration measured data. The results revealed that about 4.8 and 16.35% of the study area are classified as highly suitable and suitable areas for RWH, respectively. Around 6% of the study area (98.5 km2) was found to be suitable for farm ponds, whereas 1.6% (26.1 km2) suitable for check dams, and 25.9% (423 km2) suitable for bench terraces. Among 50 proposed sites for the RWH structures, 29 are located in the most suitable area for RWH. The results implicated that the surface runoff, sediment yield, and topography are essential factors in identifying the suitability of RWH areas. It is concluded that the integrated geospatial and MCDM techniques provide a useful and efficient method for planning RWH at a basin scale in the study area.

Central Asia is a strategic geopolitical region, which has recently been identified to be on the verge of a water -related crisis. Yet, most studies addressing water-related issues across Central Asia have lacked multi-sectoral and cross-country perspectives. This study used the multi-region input-output model to quantify the water footprint in Central Asia as well as the STIRPAT technique to study its driving forces from 1995 to 2015. The results revealed that the production and consumption-based water footprints of Central Asia sharply increased, as proven by the growth rate of 12.69 x 109 m3/annum and 9.52 x 109 m3/annum, respectively. The water -intensive agriculture sector completely dominated Central Asia's water footprint. In addition, between 1995 and 2015, the volume of imported and exported water footprints in Central Asia increased. In terms of exports, the internal water footprint accounted for 3%-5%, whereas the external accounted for 95%-97%. With respect to imports, the internal water footprint dropped from 50% to 37%. Nevertheless, the gap between export and import volumes was approximately ten-fold, which revealed that international trade exacerbates Central Asia's water crisis. Furthermore, urbanisation was revealed to have significant positive effect on water footprint changes compared to population, gross domestic product (GDP) and water use efficiency in Central Asia. This study provides policy implications for promoting Central Asia's water resource management, as well as high-lighting the driving forces of water footprint that need attention, resulting in cooperative water resource man-agement to promote sustainable development.

There is consistent evidence of vegetation greening in Central Asia over the past four decades. However, in the early 1990s, the greening temporarily stagnated and even for a time reversed. In this study, we evaluate changes in the normalized difference vegetation index (NDVI) based on the long-term satellite-derived remote sensing data systems of the Global Inventory Modelling and Mapping Studies (GIMMS) NDVI from 1981 to 2013 and MODIS NDVI from 2000 to 2020 to determine whether the vegetation in Central Asia has browned. Our findings indicate that the seasonal sequence of NDVI is summer > spring > autumn > winter, and the spatial distribution pattern is a semicircular distribution, with the Aral Sea Basin as its core and an upward tendency from inside to outside. Around the mid-1990s, the region’s vegetation experienced two climatic environments with opposing trends (cold and wet; dry and hot). Prior to 1994, NDVI increased substantially throughout the growth phase (April–October), but this trend reversed after 1994, when vegetation began to brown. Our findings suggest that changes in vegetation NDVI are linked to climate change induced by increased CO2. The state of water deficit caused by temperature changes is a major cause of the browning turning point across the study area. At the same time, changes in vegetation NDVI were consistent with changes in drought degree (PDSI). This research is relevant for monitoring vegetation NDVI and carbon neutralization in Central Asian ecosystems.

Quantifying the coupled cycles of carbon and water is essential for exploring the response mechanisms of arid zone terrestrial ecosystems and for formulating a sustainable and practical solution to issues caused by climate change. Water use efficiency (WUE), one of the comprehensive indicators for assessing plant growth suitability, can accurately reflect vegetation’s dynamic response to changing climate patterns. This study assesses the spatio-temporal changes in WUE (ecosystem water use efficiency, soil water use efficiency, and precipitation water use efficiency) from 2000 to 2018 and quantifies their relationship with meteorological elements (precipitation, temperature, drought) and the vegetation index (NDVI). The study finds that the sensitivity of NDVI to WUE is highly consistent with the spatial law of precipitation. The εPre threshold range of different types of WUE is about 200 mm or 1600 mm (low-value valley point) and 300 mm or 1500 mm (high-value peak point), and the εTem threshold value is 3~6 °C (high-value peak point) and 9~12 °C (low-value valley point). The degree to which vegetation WUE is influenced by precipitation is positively correlated with its time lag, whereas the degree to which temperature influences vegetation is negatively correlated. The WUE time lag is very long in hilly regions and is less impacted by drought; it is quite short in plains and deserts, where it is substantially affected by drought. These findings may be of great significance in responding to the severe situation of increasingly scarce water resources and the deterioration of the ecological environment across Central Asia.

Satellite-driven and site observational data show that global warming has greatly enhanced vegetation activity (greening), with a broad upward trend in vegetation indices (VIs). However, the biophysical impact of vegetation on land surface temperature (T-s) is widely unknown, particularly in High Mountain Asia (HMA). In this study, we assessed vegetation dynamics in HMA from 2000 to 2020 and used intrinsic biophysical mechanism (IBM) models to quantify the biophysical feedback effects of vegetation on T-s. The results illustrate that, during the study period, HMA has undergone greening at a rate of 0.505 x 10(-3) W m(-2) mu m(-1) sr(-1) yr(-1) (solar-induced chlorophyll fluorescence: SIF) and 1.7 x 10(-3) yr(-1) (normalized vegetation index: NDVI), accounting for 52.75% and 47.24% of the total number of pixels, respectively. However, vegetation browning has occurred in areas such as the central Tien Shan and southeastern Tibet. Meanwhile, 64.22% (SIF) and 53.68% (NDVI) of the vegetation area in HMA showed a negative sensitivity of T-s to vegetation activity, particularly herbaceous and scrub vegetation in Inner Tibet and eastern Kun Lun. Additionally, the Bovine ratio and aerodynamic drag (r(a)) exhibited a negative sensitivity to vegetation activity. Most importantly, the vegetation activity-induced temperature changes were -0.278 K (Delta T-NDVIC(IBM)), -0.538 K (Delta T-SIFC(IBM)), 0.028 K (Delta T-NDVIS(IBM)), and 0.024 K (Delta T-SIFS(IBM)). Moreover, in the sensitivity method (Delta T-VegS(IBM)), vegetation cooling was detected in more than half of the pixels in HMA and was even more pronounced with SIF, accounting for 64.22% compared to 53.68% with NDVI. Enhanced vegetation activity alters the original balance of latent and sensible heat fluxes (Le, H) and increases the turbulent heat transfer between land and atmosphere, particularly Le. Our findings have important implications for understanding the response and feedback of vegetation dynamics to climate change in arid alpine regions.

The fragile ecosystem of the desiccated Amu Darya River Basin (ADRB) has experienced profound local hydrological havoc. These changes are associated with temperature, evapotranspiration, precipitation, and human-induced effects. However, previous long-term analyses have inadequately addressed these problems at the basin scale. Therefore, this study applied the elasticity coefficient method coupled with the water balance and Budyko framework, using hydro-meteorological observation data from three stations (Termez, Atamurat, and Kiziljar) to assess the long-term (1960-2017) runoff dynamics within the ADRB. The findings revealed that runoff at the three stations decreased at rates of -0.52 mm/a, -0.80 mm/a, and -0.97 mm/a, respectively, during the study period. Additionally, the relative contribution of climate change to runoff reduction ranged from -14.25% to -5.43%, while that of human activities exceeded 100% at all three stations. In addition, the total population across the basin has doubled within 33 years to approximately 80 million people, coupled with an expansion of cropland areas at a rate of 42.6%, corresponding to a net increase of 2,520,249 ha. Furthermore, the construction of reservoirs that were implemented before cropland expansion activities reduced the amount of water flowing downstream since runoff needs to be stored before being released for irrigation. This demonstrates that long-term runoff reduction was mainly due to human activities, with construction of reservoirs for cropland expansion being the most direct factor. The study findings provide essential information for water resource management in the ADRB. More attention should be paid to the construction and operation of reservoirs, and sustainable cropland development should be implemented for improved ecological environment.

The desert-oasis ecotone, as a crucial natural barrier, maintains the stability of oasis agricultural production and protects oasis habitat security. This paper investigates the dynamic evolution of the desert-oasis ecotone in the Tarim River Basin and predicts the near-future landuse change in the desert-oasis ecotone using the cellular automata–Markov (CA-Markov) model. Results indicate that the overall area of the desert-oasis ecotone shows a shrinking trend (from 67,642 km2 in 1990 to 46,613 km2 in 2015) and the land-use change within the desert-oasis ecotone is mainly manifested by the conversion of a large amount of forest and grass area into arable land. The increasing demand for arable land for groundwater has led to a decline in the groundwater level, which is an important reason for the habitat deterioration in the desert-oasis ecotone. The rising temperature and drought have further exacerbated this trend. Assuming the current trend in development without intervention, the CA-Markov model predicts that by 2030, there will be an additional 1566 km2 of arable land and a reduction of 1151 km2 in forested area and grassland within the desert-oasis ecotone, which will inevitably further weaken the ecological barrier role of the desert-oasis ecotone and trigger a growing ecological crisis.

In an increasingly globalized and warming world, drought can have devastating impacts on regional agriculture, water resources, and the ecological environment. Reliable prediction of future drought changes is especially important within the context of rapid warming. However, the extent and future trends of drought changes are variable and incomplete in the CMIP6 forcing scenarios. Based on the CMIP6 data, we chose the standardized precipitation-evapotranspiration index to predict future global drought. The results show that when emissions increase under the three shared socioeconomic pathway (SSP) scenarios (SSP126, SSP245 and SSP585), the global climate environment becomes drier and drought grow more severe and longer-lasting. Regions already classified as arid will suffer even more severe drought under high-emission SSPs. Specifically, 36.2% of global land will experience increased drought under SSP126, including 67.0% of regions designated as arid, with droughts intensifying significantly. Under SSP585, 68.3% of global land will suffer increased drought, with 93.2% of the arid regions experiencing significant drought intensification. Furthermore, the global duration of drought is estimated to be 4.4 months, 5.7 months, and 8.6 months for the time periods 1960–2000, 2021–2060, and 2061–2100, respectively. Notably, for the SSP585 scenario, regions that are already arid may become universally drought-stricken by the late 21st century. The most severe aridification trends may occur in the arid regions of Australia, Middle East, South Africa, Amazon basin, North Africa, Europe, and Central Asia. Additionally, Europe and the Amazon River Basin are also facing the threat of future drought. Increased aridification will put these regions and countries at risk of further land and ecological degradation, as well as increased poverty. The results of this study have far-ranging implications not only for how we deal with the impacts of climate warming-induced drought disaster, but also for how these impacts affect socio-economic and ecological security.

The ability to adequately and continually assess the hydrological catchment response to extreme rainfall events in a timely manner is a prerequisite component in flood-forecasting and mitigation initiatives. Owing to the scarcity of data, this particular subject has captured less attention in Rwanda. However, semi-distributed hydrological models have become standard tools used to investigate hydrological processes in data-scarce regions. Thus, this study aimed to develop a hydrological modeling system for the Nyabarongo River catchment in Rwanda, and assess its hydrological response to rainfall events through discharged flow and volume simulation. Initially, the terrain Digital Elevation Model (DEM) was pre-processed using a geospatial tool (HEC-GeoHMS) for catchment delineation and the generation of input physiographic parameters was applied for hydrological modeling system (HEC-HMS) setup. The model was then calibrated and validated at the outlet using sixteen events extracted from daily hydro-meteorological data (rainfall and flow) for the rainy seasons of the country. More than in other events, the 15th, 9th, 13th and 5th events showed high peak flows with simulated values of 177.7 m3s−1, 171.7 m3s−1, 169.9 m3s−1, and 166.9 m3s−1, respectively. The flow fluctuations exhibited a notable relation to rainfall variations following long and short rainy seasons. Comparing the observed and simulated hydrographs, the findings also unveiled the ability of the model to simulate the discharged flow and volume of the Nyabarongo catchment very well. The evaluated model’s performance exposed a high mean Nash Sutcliffe Efficiency (NSE) of 81.4% and 84.6%, with correlation coefficients (R2) of 88.4% and 89.8% in calibration and validation, respectively. The relative errors for the peak flow (5.5% and 7.7%) and volume (3.8% and 4.6%) were within the acceptable range for calibration and validation, respectively. Generally, HEC-HMS findings provided a satisfactory computing proficiency and necessitated fewer data inputs for hydrological simulation under changing rainfall patterns in the Nyabarongo River catchment. This study provides an understanding and deepening of the knowledge of river flow mechanisms, which can assist in establishing systems for river monitoring and early flood warning in Rwanda.

The world's rapid population growth is also fueling the high demand for food. Cropland intensification and expansion as the primary source of increasing food production thrives everywhere, yet hinders natural vegetation. This study aims to examine the extent to which the development of Cropland intensity affects natural vegetation and scrutinizes its relationship with Gross Domestic Product (GDP) on the African continent from 1992 to 2015. The ratio of total Crop area harvested and Cropland extent (CE) datasets used to compute the cropping intensity CI (yr(-1)) for each year over 24 years. The correlation coefficient and comparison method were applied for cropland intensity and natural vegetation to reveal the rate at which cropland intensity affects natural vegetation. The same method was applied between CI, CE and GDP percentage from Agriculture to know their relationship. The results revealed that Cropland extended at a rate of about 9130.17 km(2) yr(-1), leading to a rise of 219,123.99 km(2) of increase. Shrubland was the most devastated natural vegetation at a rate of -9461.96 km(2) and cleared about 227,087.1 km(2). The CI and GDP percentage from agriculture showed a strong negative correlation in most of the countries, especially in developing countries. The CI is low and the CE is high, which means the degradation of existing natural vegetation and the eradication of potential environment health. Ethiopia and Tanzania have been the only two countries which experienced cropland decrease among the top 10 African countries with a large cropland area, while Nigeria and Sudan have increased the most. Further Malawi witnessed the highest percentage growth for cropland. South Sudan has identified strong trends in GDP percent from agriculture, while Libya has seen substantial decline. On the other hand, Liberia, Sierra Leone, Guinea-Bissau, Ethiopia, and Chad identified to have the biggest % of agriculture to GDP during the study period.

Estimating Terrestrial Water Storage (TWS) not only helps to provide a comprehensive insight into water resource variability and the hydrological cycle but also for better water resource management. In the current research, Gravity Recovery And Climate Experiment (GRACE) data are combined with the available hydrological data to reconstruct a longer record of Terrestrial Water Storage Anomalies (TWSA) prior to 2003 of the Tarim River Basin (TRB), based on a Long Short-Term Memory (LSTM) model. We found that the TWSA generated by LSTM using soil moisture, evapotranspiration, precipitation, and temperature best matches the GRACE-derived TWSA, with a high correlation coefficient (r) of 0.922 and a Normalized Root Mean Square Error (NRMSE) of 0.107 during the period 2003–2012. These results show that the LSTM model is an available and feasible method to generate TWSA. Further, the TWSA reveals a significant fluctuating downward trend (p < 0.001), with an average decline rate of 0.03 mm/month during the period 1982–2016 in the TRB. Moreover, the TWSA amount in the north of the TRB was less than that in the south of the basin. Overall, our findings unveiled that the LSTM model and GRACE data can be combined effectively to analyze the long-term TWSA in large-scale basins with limited hydrological data.