Globally, 60% of the evaporation from land returns as precipitation over land and a fifth of annual precipitation over land is directly dependent on the presence of vegetation-supplied moisture. In many regions, particularly in dry seasons, a majority of the precipitation relies on moisture from vegetation and is therefore vulnerable to changes in upwind land use that modify water moisture supply to the atmosphere. The benefits of precipitation for societies are invaluable, ranging from food production to carbon sequestration, and the role of ecosystems for supplying moisture for rainfall can be therefore be considered an important, albeit under-appreciated, ecosystem service.  

Our research shows that loss of moisture-supplying ecosystems, such as deforestation in the Amazon, can disrupt such moisture supplies, thereby reducing precipitation and negatively impacting crop yield, wetlands, and forest resilience in downwind regions. Conversely, some human activities, such as afforestation and irrigation, bring untapped subsoil water resources into the atmosphere and can help mitigate dry spells both locally and remotely. While they can have the potential to bring moisture-supplying benefits similar to moisture-supplying ecosystems, they also carry the risk of depleting local surface and groundwater resources and bringing about other adverse trade-offs. 

The past decade has seen rapid developments in moisture tracking models and data, which have brought to light previously ignored long-distance moisture flow relationships among different land areas, land users, and land-use decisions. These scientific advances mean that it is now possible to map out the ecosystem service of vegetation-supplied precipitation at a global scale in great detail, as well as to track their dependencies and interdependencies. 

We argue that the time is ripe for moisture-supplying ecosystems to be widely considered in land management and governance contexts. Nevertheless, a few important challenges remain. Particularly, future research needs to better constrain the uncertainties of moisture recycling relationships under climate change and atmospheric circulation change; to understand the effects of ecosystem adaptation, regime shifts, and social-ecological feedbacks; as well as to quantify the multiple benefits and trade-offs of the ecosystem service of vegetation-supplied precipitation. A better understanding of the relationships between moisture supply, drought mitigation, ecosystem resilience, and terrestrial carbon is especially relevant under the current UN Decade of Ecosystem Restoration as well as for achieving the Paris Agreement temperature target.

How to cite: Wang-Erlandsson, L., Keys, P., Staal, A., Theeuwen, J., Wunderling, N., Dekker, S., Pranindita, A., Teuling, A. J., Nyasulu, M. K., Fetzer, I., Flach, R., Lathuillière, M. J., Fahrländer, S., Jaramillo, F., Gordon, L., Singh, C., van der Ent, R., Posada, J. A., Moore, M.-L., and Cao, M.: Time to recognize the ecosystem service of vegetation-supplied precipitation in management and governance , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19880, https://doi.org/10.5194/egusphere-egu24-19880, 2024.

Newer water management approaches aim to better utilize runoff by treating it as a resource rather than a nuisance and by promoting a more holistic handling of runoff. These approaches use nature-based green infrastructure solutions to increase infiltration and detain water. Identifying optimal placement for these solutions is challenging due to multiple and sometimes competing objectives. Here, we propose a methodology to help planners and stakeholders maximize the benefits of flood mitigation projects by identifying opportunities for sustainable development. Most studies examine placement decisions based on metrics obtained from hydrological models. However, solely depending on hydrological indicators, without accounting for social and ecological indicators, might bias the placement decisions. We propose combining hydrological and land-use planning models. We used the revised version of the Soil and Water Assessment Tool (SWAT) known as SWAT+ to simulate existing hydrological conditions and the results as initial input for the conservation decision support software MARXAN. We added data on endangered species and distance from the human population as ecological and social indicators. This addition shifted the selected areas and provided a more complete view of runoff management than only hydrological indicators. Furthermore, we show that coupling SWAT+ and MARXAN can effectively balance hydrological concerns with ecological and social factors.

How to cite: Tal-maon, M., Broitman, D., Portman, M. E., and Housh, M.: Combining a hydrological model with ecological planning for optimal placement of water-sensitive solutions, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-454, https://doi.org/10.5194/egusphere-egu24-454, 2024.

Over the past few decades, the Curve Number (SCS-CN or CN) approach has been employed to estimate direct surface runoff. In the times of urban sprawl, rapidly growing socioeconomic anthropogenic activities, and environmental changes all have a rapid growth that leads to spatial and temporal variability in the land use/land cover (LULC) complex which in a way affects the direct surface runoff, LST, and Vegetative cover. The study utilized the pixel based (PB), object directed (OD) machine learning algorithm i.e., Random Forest (RF) classifier for the LULC change study of the Google Earth Engine (GEE) platform for the verification of the SCS-CN, LST, and vegetative cover variability over 4 decades from 1980 to 2020 of the Ong River sub-basin (area = 4650 sq. km) of the Mahanadi River Basin of India. Sentinel-2 and Landsat satellite products were processed and utilized to conduct the LULC change analysis. The Kappa Coefficients of LULC maps for each decade from 1980 to 2020 equaled 0.86, 0.90, 0.891, and 0.895 with overall accuracy percentages of 97.89, 96.16, 96.79, and 96.44, respectively. The study determined the associated effects of each LULC class (i.e., built-up areas, Barren land, Water Bodies, Cropland, and Forest) on CN variability, LST, and vegetation index i.e., Normalized difference vegetation index (NDVI). The CN values varied from 64 to 78 in the 4 decades, suggesting the effects of decrease in forest cover and an increase in the built up. The LULC change analysis revealed a considerable decrease in the forest area from 1864.8 sq. km to 1098.34 sq. km and a sizeable increment in built-up area from 123.3 sq. km to 458.9 sq. km.  Furthermore, the study investigated and correlated each LULC class with the LST and NDVI. LST and NDVI for the forest and built-up areas were correlated with R² equal to 0.91 and 0.72, respectively. Overall, the results suggest considering dominating LULC class change led to a rise in the average LST of the watershed from 28.06 °C to 28.68 °C. The increase in the average LST and increasing Curve Number value is a consequence of the scanty forest area combined with the reduction in the greenness of vegetative cover due to an increase in the built-up area indicating the alteration in the land-use patterns, land management practices, and streamflow due to anthropogenic activities. This type of study is helpful for watershed planning and management.

How to cite: Prashant, P., Kumar Mishra, S., and Kumar Lohani, A.: Investigating the effects of Changing Land Use/Land Cover on Curve Number, Land Surface Temperature, and Vegetative Cover in the Ong Sub-Basin of the Mahanadi River, India, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-884, https://doi.org/10.5194/egusphere-egu24-884, 2024.

Ecosystems provide various direct and indirect benefits for human society, which are closely intertwined with human life, health, and economic well-being. However, pressures such as climate change, changes in land use/land cover, and human activities may pose threats or undermine the stability of ecosystems, consequently impacting the provision of ecosystem services. In recent years, climate change has altered precipitation patterns and increased the occurrence of extreme weather events, resulting in variations in watershed dynamics and affecting the stability of water resources. The impact on water resources has intensified, leading to changes in watershed ecosystem services and influencing the quality of human life and property security. This study is based on simulations using the Soil and Water Assessment Tool (SWAT) model in the Jhuoshuei River basin. Simulations cover historical periods (2002-2020) and near-future (2021-2040) and far-future (2081-2100) scenarios under RCP2.6 and RCP8.5 of CMIP5 models. Five indicators, which include freshwater provision index (FPI), green water scarcity (GWS), green water vulnerability (GWV), flood regulation index (FRI), and erosion regulation index (ERI), were selected for quantitative assessment and analysis of dry and wet season water provisioning and regulating services in the Jhuoshuei River basin. Spatial autocorrelation analysis was employed to identify high and low zones of ecosystem services, and Spearman correlation analysis was used to determine the trade-offs and synergies among the five ecosystem services indicators. The results indicate significant differences in the five indicators between dry and wet seasons. The FPI performs better in the wet season, while the other four indicators exhibit better performance in the dry season. Across historical and climate change scenarios, the relationships among the five indicators remain consistent. The FPI demonstrates a trade-off relationship with the other four ecosystem services, while the FRI, ERI, GWS, and GWV exhibit mutual synergies. This study shows the intercorrelations among the ecosystem services related to water resources, and the results serve as the references for water resources regulations and watershed management.

Keywords: SWAT model, Provisioning services, Regulating services, Green water security, Jhuoshuei River basin

How to cite: Yu, Z.-R., Yuan, J.-L., Chiang, L., and Wang, Y.-C.: Investigating the intercorrelations of ecosystem services related to water resources at the catchment scale, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5248, https://doi.org/10.5194/egusphere-egu24-5248, 2024.

Jordan has experienced several flash floods in recent years, causing property damage and fatalities. Because of the high rates of urbanization since the 1950s, flash flood damage is more likely and the risk to infrastructure and people is increasing. However, the contribution of land use and land cover changes (LULCC) to flash flood risk in urban areas is still poorly understood. Our aims were, therefore, to (i) examine LULCC and urbanization trends and their drivers in Amman, the capital of Jordan (ii) simulate future land cover trends and (iii) investigate the impact of urban expansion on flash flood related damages in the past, present and future.

A mixed-method approach combining quantitative (remote sensing, statistical modelling) and qualitative methods (semi-structured interviews and stakeholder workshops) was used. Past long-term LULCC from 1968 to 2021 were analyzed via object-based classification of panchromatic Corona and multispectral Spot images. Semi-structured expert interviews were conducted to explore historic and current LULCC drivers and their dynamics. The simulation of future land cover trends was based on past LULCC and the identified main drivers using an MLP-MC model then refined with local experts’ knowledge of future urban planning through stakeholder workshops. The resulting LULC-maps were used in hydrological modeling with HEC-HMS to assess LULCC effects on runoff generation.

In the last six decades, the built-up area in Amman's watershed has increased significantly, by a total of 203 km² between 1968 and 2021 (from 20 km² to 223 km²). This trend was mainly at the expense of rainfed cultivated plots and green retention areas (157 km² from 1968 to 2021), resulting in reduced water infiltration and accelerated rates of runoff. The LULCC and urbanization patterns stem from intricate feedback loops involving various socio-economic and bio-physical drivers. Key urbanization drivers include demographic trends (population growth and density), topographic conditions (slope, elevation) as well as the accessibility and location of an area (density of former built-up area, distance to formal/informal refugee settlements, major roads, built-up areas, city centers, and employment hubs). The inadequate policy interventions coupled with mismanagement and unbalanced land-use allocations highly influenced the urbanization pattern that has favored the loss of retention areas in the past. For future land cover changes, the conducted stakeholder workshops revealed that development plans will focus particularly on the dry eastern region of Amman’s watershed (areas that generally receive less rainfall). However, measures and actionable plans to maintain the remaining retention areas are still lacking. The simulated future land cover trend indicates a continued loss of retention areas while areas prone to flash floods will increase emphasizing an urgent need to integrate flash flood risk management in urban development plans. Here, our LULCC analysis gives decision support for urban planners, especially for spatial planning and allocation of future measures and retention areas.

How to cite: Awad, A., Hohmann, C., Maus, C., Abu Hamour, W., Al Naimat, M., and Brinkmann, K.: Understanding urbanization's impact on flash flood risks from past to future: A Case study of a rapidly growing MENA city, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7592, https://doi.org/10.5194/egusphere-egu24-7592, 2024.

The African Great Lakes Region has experienced substantial Land Use and Land Cover Change (LULCC) over the last decades, significantly impacting landscapes and ecosystems. The main drivers of LULCC in the region involve a complex interplay of political, economic, and socio-demographic factors. This study focused on the Lake Kivu catchment in Rwanda, a critical ecosystem in the African Great Lakes Regions, exploring both historical and future LULC scenarios. The methodology involved supervised image classification using seasonal composites and integrating spectral indices with topographic features to capture dynamic seasonal variations. Historical LULCC analysis showed significant changes, particularly the first decade of the study (1990-2000) marked by substantial forest loss (from 26.6% to 18.7%) and a notable increase in agricultural land (from 27.7% to 43%). These changes were attributed to conflicts in the region and population movements. Subsequent decades (2000-2010 and 2010-2020) witnessed marked forest recovery (24.8% by 2020) and a balance between agricultural increase and loss, reflecting Rwanda's commitment to environmental conservation. Additionally, a multi-layer perceptron artificial neural network was employed to predict future LULC scenarios, considering natural and socio-economic explanatory variables with historical LULC transitions. The predicted future LULC for 2030 and 2050 indicate distinct trajectories influenced by factors like political will, demographic trends, and socio-economic developments. By integrating the observed historical trends and predicted future LULC, along with climate scenarios, this study will use a hydrological model to understand the impacts of these changes on the catchment’s hydrological components. Providing essential insights for policy and strategic planning, we explore how the intricate dynamics of water-related ecosystem services are influenced by LULC and climate changes, with the ultimate goal of harmonizing ecological sustainability with socio-economic development in the Lake Kivu catchment and similar environments.

How to cite: Kayitesi, N., Guzha C., A., Tonini, M., and Mariethoz, G.: Spatio-temporal evolution of Land Use Land Cover and hydrological components in the Lake Kivu catchment, Rwanda., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9350, https://doi.org/10.5194/egusphere-egu24-9350, 2024.

Among all the renewable energy sources, solar photovoltaic (PV) is one of the most widespread in the word. Although solar energy is universally recognised as environmentally friendly energy source, impacts on surface hydrology of large parks have not been comprehensively addressed in literature. With growing concern over the impact of land use changes on stormwater runoff, the construction of large-scale solar power plants may face obstacles in the future unless appropriate quantification of this impact is addressed, and proper measures are taken to mitigate potential increment of flow peak and volume discharge. Assessment of runoff generation in PV solar parks can be carried out by modelling-based approaches, that have the advantage, with respect to purely experimental studies, to allow the investigation of the influence of different hydrological conditions. Moreover, the modelling-based approach enables the possibility to assess the park behavior in the short and in the long-term conditions. Analysis of the literature on the topic highlights a research gap consisting in the lack of a comprehensive tool for the assessment of the impacts of real-scale solar parks on stormwater runoff, by considering the hydrological processes occurring within the park and all the variables affecting the park response to precipitation events. In this work, we propose a novel conceptualization of PV solar parks response to precipitation events capitalizing on the use of the free and open-source Storm Water Management Model (SWMM). The conceptualization allows to take into account the complex hydrological process occurring in the solar parks during precipitation events and to assess how the process of runoff in the park is affected by the extension of the PV installation, soil properties and the characteristics of the rainfall events. Moreover, effects of long-term changes in roughness surface induced by the presence of the panels are taken into account in the analysis. We demonstrate the potentialities of the proposed approach considering a layout of the PV installation (panels size and inclination) as well as characteristics of the precipitation events that are encountered in Sicily (south Italy). In all the simulations, outflow discharge from the park is compared to that from a reference catchment to evaluate variations of peak flow and runoff volume. Results highlight no practical changes in runoff in the short term after installation. However, in the long term, modifications in soil cover may lead to some potential increase of runoff. For instance, increments of the peak flow from the solar park up to 21% and 35% are obtained for roughness coefficient reductions of 10% and 20%, respectively. The proposed modelling approach can be beneficial for studying hydrological impacts of solar parks and thus for planning measures for their mitigation.

How to cite: Gullotta, A., Aschale, T. M., Peres, D. J., Sciuto, G., and Cancelliere, A.: Towards assessing the impacts of large ground-mounted solar parks on the hydrological cycle: an analysis of runoff changes with EPA-SWMM software , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11451, https://doi.org/10.5194/egusphere-egu24-11451, 2024.

Quantification of the impact of Land Use and Land Cover Change on ecosystem services is crucial for identifying critical areas requiring conservation efforts and sustainable land practices. Landscape metrics, which quantify the spatial arrangement of land cover types within a landscape, have emerged as valuable tools for systematically understanding such impacts and operationalize land use changes. In this study, we explored the contribution of landscape metrics to predicting water-related ecosystem services, mainly water provisioning at the watershed scale represented by runoff. We employed a random forest model to approximate   distributed  maps of runoff  for the Arno River Basin in Italy, obtained from a nationally used gridded hydrological water balance model .   Open access earth observation data were used as environmental predictors, including hydroclimatic variables, land use classes, and of landscape metrics related to runoff. The out of bag error is used to assess model performance along with variance and bias, and a leave one sub-watershed out a time is used for validation.  Our results demonstrated that despite a relatively low feature importance compared to other direct hydrological predictors like precipitation and temperature, landscape metrics, especially the core area index of forest and agricultural land use, captured significant interactions between forest and agricultural land use and their influence on water provision. In wet and normal conditions where precipitation is the predominant factor, the significance of these metrics intensifies, whereas in dry conditions characterized by dominant groundwater recharge processes, their relevance diminishes.  Leveraging land use data and earth observations, this approach clarifies the complex LULCC-ecosystem service relationships, informing strategies that balance ecosystem multifunctionality with environmental sustainability despite limitations in comparison to process based models.

How to cite: El Jeitany, J., Nussbaum, M., Pacetti, T., Schröder, B., and Caporali, E.: Relevance  of Landscape Metrics in Predicting Water-Related Ecosystem Services demonstrated on the Arno River Basin Italy, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15309, https://doi.org/10.5194/egusphere-egu24-15309, 2024.

Landscapes are constantly changing under the pressure of human activities or climate instability. In particular, variations in vegetation cover have a significant impact on the hydrological cycle and the response of the landscape to hydrometeorological forcing. Greenery depletion caused by human activities, such as deforestation for agricultural purposes or uncontrolled urbanisation, or by climate, such as desertification, can lead to an unbalanced hydrological partitioning, exacerbating runoff production. In recent years, an increased awareness and concern about more frequent climate extremes has led scientists and decision-makers to seek solutions for restoring degraded ecosystems. Among these, targeted land use and land cover (LULC) changes, such as regreening initiatives aimed at increasing vegetation coverage and the associated ecosystem services (ES), have been shown to be effective for restoration purposes. Once planned and implemented, it is imperative to have tools to assess the effectiveness of such LULC changes and the associated impacts on the hydrological behaviour of the restored landscape.

The aim of this study is to present HydroSENS, an algorithm for the automated tracking of the spatio-temporal evolution of vegetation based on multispectral satellite imagery. Furthermore, the algorithm can be adopted for the monitoring of water-related ES concerning infiltration capacity and runoff management.  

HydroSENS allows the user to evaluate the composition of the satellite imagery, calculating the greenery fraction and properties through a spectral unmixing analysis, and retrieve hydrological parameters at the sub-pixel scale. Thus, these features make it a flexible tool for quantitatively understanding the impacts of LULC changes on hydrological processes and associated water-related ES.

The approach has been used to assess and monitor the effectiveness of a regreening project on a portion of land severely afflicted by land degradation as a result of unsustainable land management and climate changes in Tanzania. The project promoted the adoption of agroforestry practices by implementing rainwater harvesting techniques to improve localized water retention and increase the likelihood of survival of seasonal and perennial vegetation.

The site has been monitored for several years by analysing Sentinel-2 images acquired during both the wet and dry seasons. The monotonous positive trend of the Normalized Difference Vegetation Index (NDVI) and vegetation fraction over the period, in both the wet and dry seasons, indicates an increase in healthy vegetation.

The LULC alteration significantly influences the hydrological processes at the site scale, implying a high infiltration rate and an overall reduction in runoff. The benefits are twofold: first, better runoff management, reducing the consequences of flooding during heavy storms; second, larger water intake, decreasing water stress during dry spells. In addition, these regreening-induced effects improve the evapotranspiration capacity of the vegetation, maximising the crop yield as a favourable implication for the local populations.

How to cite: Tamagnone, P., Campo, C., and Schumann, G.: An automated remote sensing approach for monitoring hydrological impacts of vegetation cover changes , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16091, https://doi.org/10.5194/egusphere-egu24-16091, 2024.

Changes in land use can influence flood hazard, as land cover is one of the main factors determining runoff during an extreme rain event. Flood-regulating ecosystem services (FRES) are generally evaluated to assess how much water the environment can hold in a storm event, providing a reduction of runoff and flood hazard in a given river basin. Agriculture areas can be seen as part of the ecosystem that provides flood regulation, as they decrease the runoff with respect to barren areas or urbanized surfaces.  Methodologies developed to evaluate FRES usually assess the reduction of runoff due to land use changes without considering differences in initial soil moisture conditions before the storm event depending on crop rotation or irrigation management during the year.

Here we provide a methodology to assess FRES on a seasonal basis, evaluating crops and irrigation management practices that may exacerbate flood hazards in small agricultural river basins. We do so by coupling two hydrological models. Watneeds, an agro-hydrological model which determines the water demand in agriculture, is used to derive daily soil moisture conditions. Mobidic, a fully distributed rainfall-runoff model, sets the soil moisture conditions of Watneeds as initial soil moisture before an extreme event to evaluate the associated flood hazard.

We test the methodology on the upper Ombrone Grossetano river basin (Tuscany, central Italy), as more than 60% of its land cover is represented by agricultural areas. Gridded dataset and ground measurements are used to inform and calibrate the models and to perform the analysis. Particularly, the Chirps dataset is bias corrected with ground observation and used to perform the hydrological balance in Watneeds. Rain gauge measurement provided by the Hydrological Regional Service are used in a frequency analysis of extreme rainfall to extract the rainfall quantiles to be modeled in Mobidic.

Results show that each crop provides different soil moisture conditions under identical meteorological conditions, impacting the flood hazard accordingly. Indeed, different agricultural scenarios and practices may produce different responses to similar events in different seasons with potential management applications. This underlines how FRES should be evaluated seasonally in agricultural river basins and how crop selection, irrigation scheduling and crop calendarization in small agricultural river basins could be subject to policies that also consider potential impacts on flood risk management.

This work is part of the FLORAES project, funded by the Premio Florisa Melone 2023, awarded by the Italian Hydrological Society to stimulate independent research and collaboration among young Italian Hydrologists.

How to cite: Galli, N., Lompi, M., Rulli, M. C., and Caporali, E.: Improving flood management assessing seasonal flood regulating ecosystem services , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16661, https://doi.org/10.5194/egusphere-egu24-16661, 2024.

Droughts jeopardize both ecosystem services and economic productivity within river basins - especially as their frequency and intensity are expected to increase as a result of climate change. One aim of climate adaptation measures is therefore to increase resilience through water retention in landscapes. In this contribution we aim at identifying suitable land use adaptation measures to increase drought resilience. To this end, the eco-hydrological model SWAT+ is used to analyze the hydrological response of different land use classes under drought conditions in the Lippe catchment in Germany. The model represents catchment specific land use information on crops and crop rotations, tree species and imperviousness of urban and industrial areas. Anthropogenic effects such as water exchange with the Western German transportation canal network and tile drainages are also modeled. The model is calibrated specifically with regard to representing low flow periods based on available discharge measurements from several gauges. The hydrologic response of different land use classes shows variations in evapotranspiration, soil moisture, ground water recharge as well as surface runoff and lateral flow during a drought period. This illustrates the potential of land use changes to improve drought resilience, by promoting land uses that increase water retention. Based on these findings, further research will be conducted to investigate the potential of combined land use measures to increase resilience on the catchment scale and under different climate and adaptation scenarios. In the future, the modeling approach will serve as a planning tool for river basin management of the Lippe and the potential to transfer these measures to other catchment areas will be determined in the KliMaWerk research project funded by the German Federal Ministry of Education and Research as part of the "Water Extreme Events" (WaX) research programme.

How to cite: Grantz, S. F., Wagner, P. D., Kiesel, J., and Fohrer, N.: Assessing the potential of land use adaptation measures with eco-hydrological modelling to increase drought resilience., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16978, https://doi.org/10.5194/egusphere-egu24-16978, 2024.

The most water-stressed basins accommodate almost one third of the global irrigated agriculture. Future food production in these basins is threatened not only by excessive water consumption but also by climate change impacts that endanger irrigation water availability and increase water requirements of crops through increasing evapotranspiration (ET). Endorheic river basins in Central Asia are particularly susceptible to climate change. Large-scale irrigation projects during the Soviet period, mainly for water- intensive cotton cultivation, already contributed to the rapid desiccation and salinization of the Aral Sea, formerly the world’s fourth-largest inland water body. Changes in cropping practices after the collapse of the Soviet Union in 1991 included planning less water intensive winter wheat along with an increase in cropping frequency, however the impacts of these changes in land use and of climate change on water consumption remain unknown. Here, we estimated the spatial and temporal dynamics of crop ET and its driving factors across the Amu Darya basin, from 1987 to 2019 using hydrometeorological data, Landsat imagery, yearly maps of cropping practices, and ET estimations derived from the Operational Simplified Surface Energy Balance (SSEBop) model. The results show an overall increase of 20% in crop water consumption despite the decrease in water intensive cropping. Downstream countries Turkmenistan and Uzbekistan have the highest contribution to water consumption. Although changes in cropping practices contributed positively to water demand, annual ET increased in the last 30 years in accordance with the exacerbating temperature rise. Our study provides the first long-term and high-resolution analysis of crop water consumption in the Amu Darya Basin which can support water managers and policy makers towards improved water management decision and planning in a changing climate. 

How to cite: Peña-Guerrero, M. D., Umirbekov, A., Tarasova, L., Rufin, P., Senay, G., and Müller, D.: Long-term dynamics of crop water consumption in the irrigated lands of the Amu Darya basin , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20916, https://doi.org/10.5194/egusphere-egu24-20916, 2024.