Water resource evaluation and management rely heavily on detailed and well-classified data on water supply, demand, consumption, and withdrawals. The Water Accounting Plus (WA+) framework provides an effective platform to anticipate water flows by integrating remote sensing data to analyze water flows within a basin while accounting for land use. For the Mahi River, we used the WA+ framework to get insights about water inflows, outflows, consumption, withdrawals, and storage changes. The WaterPix (soil moisture water balance) model was employed to simulate hydrological processes at the pixel level. Here, we also employed blue and green water estimates to classify between irrigated and rainfed regions. The present state of the basin's water resources was also computed using performance indicators. As per our study, the Mahi River basin experiences water scarcity and heavily depends on groundwater (GW) for agriculture. From 2012-2020, there has been an average annual outflow of 20.65 BCM, with average annual flows of exploitable and available water being 34.07 BCM/year and 30.38 BCM/year, respectively. Furthermore, the average vertical GW recharge and outflow were 17.47 BCM/year and 21 BCM/year, respectively. Though, the average surface water (SW) withdrawal was lower and concentrated in a few regions. The outcomes from the GW sheets demonstrated a considerable dependence on GW, with 95% of the used flow coming from GW. Notably, over the study period, there was a 25% reduction in water storage, emphasizing the challenges of excessively using GW for irrigation and the decrease in water storage within the Mahi River basin. The conclusions of our study give local and national authorities important new information that they can use to spot regions with poor water management practices and create water management strategies and programs that are suitable for the basin's requirements.   

How to cite: Patle, P. and Sharma, A.: Assessing Water Resource Availability and Utilization in the Mahi River Basin: A Comprehensive Analysis using Water Accounting Plus (WA+), EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-839, https://doi.org/10.5194/egusphere-egu24-839, 2024.

    Excessive applications of phosphate fertilizers have led to significant phosphorus (P) accumulation in agricultural soils. This surplus P is prone to being lost through surface runoff, thereby threatening downstream water bodies such as aquifers, streams, lakes, and oceans. Indeed, the runoff loss of P has led severe global environmental concerns, including proliferation of harmful algal blooms, onset of eutrophication, and expansion of anoxic dead zones in coastal marine ecosystems. Assessing the spatial distribution of total P (TP) runoff loss from croplands is essential for developing targeted mitigation strategies against the persistent issue of nonpoint source pollution. In this study, we compiled 812 datasets from 114 peer-reviewed papers for cropland P loss across China. We then developed machine learning (ML) approaches to estimate the temporal and spatial variations in P runoff loss across China from 1990 to 2020. Four prevalent ML models were considered, namely, multiple linear regression (MLR), random forest (RF), classification and regression trees (CART), and boosted regression trees (BRT). Among these four models, RF exhibited the highest predictive accuracy for both uplands (calibration: R² = 0.86, n = 293; validation: R² = 0.61, n = 96) and paddy fields (calibration: R² = 0.88, n = 137; validation: R² = 0.60, n = 44). According to RF, China’s croplands are estimated to have lost an average of 148 ± 27 Gg P yr⁻¹ from 1990 to 2020, with uplands and paddy fields contributing 114 ± 26 Gg P yr⁻¹ and 34 ± 4 Gg P yr⁻¹, respectively. The data showed a significant increase in upland TP runoff loss over the study period (p<0.001), whereas paddy field TP loss remained relatively constant. Regions in southern, eastern, and southwestern China, notably in Hainan, Guangxi, and Fujian provinces, were identified as hotspots of TP runoff loss. Scenario predictions suggest a 1.4-11.8% reduction in TP runoff loss under various conditions, most effectively when minimizing runoff depth. To effectively mitigate TP runoff loss in China, an integrated management approach involving water, soil, and fertilizer is recommended. Overall, this study enhances our quantitative understanding of cropland TP runoff loss in China, providing crucial insights for efficient cropland P management, which is key to managing nonpoint source pollution on a national level.

How to cite: Pan, Z.: Quantifying spatiotemporal variations of cropland phosphorus runoff loss in China with machine learning algorithms, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2638, https://doi.org/10.5194/egusphere-egu24-2638, 2024.

Groundwater is a key water resource. In Malawi it provides 82% of domestic, agricultural, and industrial water needs. However, despite its central importance to meeting social and economic targets, with over 14 million people reliant on groundwater to meet their everyday needs, the ‘unseen’ nature of groundwater makes management a challenge. Furthermore, minimal groundwater monitoring and measurement limit understanding of Malawi’s of water security. To guide water management policy and practice, comprehensive modelling of Malawi’s water resources, accounting for groundwater, is necessary. Here, to the best of our knowledge, we present the first process-based model of groundwater storage for the Lake Malawi Shire River Basin,which covers 94% of Malawi’s surface area, confirming prior estimates of groundwater storage.

We apply a global hydrological model, the Community Water Model (CWatM), to Malawi. To effectively represent Malawi’s water resources, we couple a high-resolution CwatM (5 arc minute resolution) with MODFLOW (5km resolution), enabling a high-resolution, national surface and groundwater model. Semi-structured stakeholder interviews were conducted to accurately represent Malawi’s water governance, identifying key adjustments that reflect national water resources. Model modifications were implemented based on stakeholder engagement. Notably, we implement model modification to account for small-holder agriculture and ‘dambo’ wetlands. National characteristics of water and sanitation were also included; the model was developed to include pit-latrine sanitation, used by over 90% of the population. Spatial variation domestic water use, both source and quantity, between urban and rural areas was also incorporated. Such model modifications significantly improved model performance, we suggest similar developments should be considered in modelling national water resources in other southern-African countries. 

Basin-wide scale model validation was undertaken by comparison with remote sensing observations of evapotranspiration, precipitation, and changes in total water storage (using GRACE Satellite data). Model calibration was undertaken by comparison to Global Data Runoff Centre (GRDC) discharge data.

We model that 660km³ of available groundwater is stored within aquifer units in Malawi (the currently available estimate of groundwater storage in Malawi is between 96.7 and 1,108 km³). Our model shows a consistent decline in groundwater levels since 1960 (the beginning of our study period). In total, we estimate a decline of 11.6km³ in groundwater storage in Malawi since 1960, raising significant concerns for future water security in the country. Not only does this model provide unprecedented insight into Malawi’s water security, particularly regarding the unseen but critical groundwater resource, further model development will enable forecasting of future water security issues under climate and socio-economic change.  

How to cite: Hinton, R., Smilovic, M., Fridman, D., Willaarts, B., Banda, L., Macleod, K., Troldborg, M., and Kalin, R.: A stakeholder driven, holistic water resources model for Malawi: applying the CWatM hydrological model., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5783, https://doi.org/10.5194/egusphere-egu24-5783, 2024.

Climate change has different impacts on water resources, altering hydrological cycles and exacerbating water-related challenges. Changes in precipitation patterns, including more frequent and intense droughts in semi-arid areas contribute to water scarcity and unpredictable availability. Being situated in central-western Morocco, the Souss basin (SB) is subject to high variations on many different scales and is strongly influenced not only by the variability of the climate but also by anthropogenic activities. SB is a strategic watershed that has considerable economic potential, mostly related to the agricultural sector, and has the typical assets and challenges of most Mediterranean watersheds. Allowing the best use of the limited water resources helps in planning better conservation and management strategies.  In this study, a DSS approach based on the ModSim-DSS model and recorded data about physical processes, hydraulic infrastructure features, and crop management was used to simulate the response of SB to climate change during the period 1990–2022. Observed rivers flow data were used to force the modeling framework over the study area. The results showed that the extent of climate change has had repercussions across the entire basin, particularly in terms of flow regimes and dam inflows. The simulation period witnessed a considerable decrease in the supply levels for the two most important dams in the region. Over the period between 2012 and 2019, there has been a notable reduction in water supplies for the Aoulouz dam, declining from an average of 100 Mm3 to 10 Mm3, representing a significant 52% decrease. Similarly, the ABDMNN dam experienced a substantial drop in water availability during the same period, decreasing from an average of 20 Mm3 to 3 Mm3, indicating a remarkable 89% decline. The differences among different supply sources fluctuate during the simulation period, resulting from changes in the available water inputs each year. The modelling approach used in this work helped identify the Souss basin’s potential challenges for best consideration in future sustainable water management plans.

How to cite: Attar, O., Brouziyne, Y., Bouchaou, L., Ait Brahim, Y., and Chehbouni, A.: Integrated modeling of climate change impacts on water resources: The Souss basin in Morocco, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5972, https://doi.org/10.5194/egusphere-egu24-5972, 2024.

The Dutch National Hydrological Model currently consists of five physically different models linked together. The software components are slated for renewal which provides a rare opportunity to improve process description, flexibility, robustness, and computational efficiency of the hydrological concepts used for national simulations of the Netherlands. Through application of the current model for integrated modelling in the Netherlands we have identified three main challenges:

• Representative parametrization of the regional groundwater-surface water interaction
• Simulating highly managed large- and small-scale water bodies in integrated hydrological simulations
• Simulating the unsaturated zone in integrated hydrological simulations of lowland regions

We will present (and would like to discuss) a 4-year PhD research plan to tackle these challenges.

The Netherlands is characterized by a dense network of surface waters for drainage requiring very fine meter-scale spatial discretization, which is unfeasible for national modelling. An analytic solution will be investigated and tested across the Netherlands to find effective parameters of the groundwater-surface water interaction and to understand its scaling behavior, providing better initial estimates and more insight to judge calibration results.

Secondly, a new code implementation to efficiently simulate highly managed surface water bodies in for regional and national applications, that is explicitly designed to be coupled to a saturated zone model, has been developed (Ribasim.jl, https://deltares.github.io/Ribasim/) and will be tested against measurements. We will focus on drought events, the groundwater-surface water interaction, and the trade-offs between (managed) surface water versus groundwater extraction.

Finally, we will compare different unsaturated zone concepts for a representative set of Dutch soil profiles, most with shallow water tables, and investigate the potential of scientific machine learning to provide computationally efficient, explainable simulation schemes. Drought events are also the primary interest here, with the aim to eventually estimate and understand crop and vegetation impacts.

The PhD project is being carried out under the Dutch Science Foundation (NWO) KIC-call ‘Climate-robust production systems and water management’ which focuses on research into solutions for robust agricultural systems, designing climate-robust and valuable nature-based sand landscapes of the future, increasing freshwater availability in coastal areas and modelling, monitoring, and predicting drought.

How to cite: Bootsma, H., van der Ploeg, M., and Weerts, A.: Renewal of the Dutch National Hydrological Model, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10686, https://doi.org/10.5194/egusphere-egu24-10686, 2024.

Water governance runs from international organizations to local consortiums through national governance. Thanks to the implementation of water policies and laws, governments are expected to ensure both (water-dependent) food and energy security without undermining environmental protection. Nowadays, agriculture accounts for 70% of the global freshwater withdrawals, and, due to the world population growth and dietary changes, this value is expected to increase. In fact, despite irrigation systems being present only in 20% of the cultivated areas, irrigated crops account for 50% of the total world’s food production. This sets the basis for local water management requirements even in generally water-rich countries. Future alterations of the hydrological cycle due to climate change are expected to further emphasize the need for water governance as they are going to affect the availability of natural water resources.

Under these conditions, an effort is required to improve national agricultural water management efficiency and to reduce agriculture's vulnerability to climactic variability. Correct water management has, in fact, the potential benefit of regulating local withdrawals from water bodies, limiting excessive use of irrigation water, and reducing crop losses due to water stress.

In this framework, agro-hydrological models can be a powerful decision-supporting tool to evaluate water requirements by agriculture at different scales. In this direction, we propose the physically-based, agro-hydrological model WaterCROP. It describes the main components of the soil-atmosphere-plant continuum (such as effective precipitation, leakage, evapotranspiration, etc.) as a function of soil, crop, and period during the growing season. The hourly temporal resolution, the spatial scales (which can span from municipal to national), and the requirement for generally accessible input-data strike a balance between highly complex and simplified models. The first ones are usually site-specific, computational-demanding hydrological models that require very detailed inputs hardly available at the national scale, while the latter, being large-scale models, provide generally too coarse results for water management decision-making.

We apply the WaterCROP model to the Italian case, showing its use to describe irrigation water-saving scenarios both for cereals crops (wheat and maize) and cash crops (vine and olives).

How to cite: Chiesa Turiano, N., Tuninetti, M., Laio, F., and Ridolfi, L.: WaterCROP, an agro-hydrological model as a decision-supporting tool for irrigation water management, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11639, https://doi.org/10.5194/egusphere-egu24-11639, 2024.

Globally, there are ongoing efforts to secure a consistent and sustainable freshwater supply, with a crucial focus on optimizing the use of existing water resources. An important strategy in this endeavor involves exploring unconventional water resources, such as the reuse of treated wastewater (TWW). Currently, de-facto (or indirect) reuse of TWW is prevalent, characterized by the extraction from surface water bodies for various purposes, including the irrigation of crops in agricultural regions. The widespread nature of de-facto reuse poses a challenge, especially since irrigation activities often coincide with dry conditions, during which TWW constitutes a substantial proportion of many surface water bodies. Hence, these practices could contribute to notable pollution concerns, potentially serving as one of the pathways for the introduction of contaminants into the groundwater system, as well as the soil-plant systems, eventually entering the food chain.

A notable gap in existing research lies in the absence of consideration for the spatial relationship between irrigated fields, affected by TWW of varying quality, and its emission source, namely the wastewater treatment plant (WWTP). Conducting this type of analysis holds the potential for a dual interpretation: i) comprehending the influence of different WWTPs contributing to pollution dispersion on individual fields, and ii) understanding the impact of a specific WWTP on all fields utilizing the discharged TWW from that particular facility.

To explore this issue in the Netherlands, we leverage prior research and use the outcomes of the Water Framework Directive (WFD) Explorer, a national water quality model used for policy support. The model employs a simplified version of the advection-diffusion equation to simulate reactive transport processes from contaminant sources—in our case, 363 WWTPs—throughout the national surface water network under steady-state flow conditions. We assume a constant flux of 1000 [g/s] of a conservative tracer emitted from each WWTP and examine its transport – from the point source to 18927 surface water units (swu), 33015 surface water abstractions (swa), and toward 366886 irrigated fields - during representative wet and dry periods in the Netherlands. Specifically, for spatial linkage, we assume buffer zones of 500m to connect relevant swu with swa and, consequently, irrigated fields.

The spatial correlation and tracer propagation, primarily influenced by the dilution effect, offer direct insights into point i). To address point ii), we introduce the Spatial Impact Indicator (SII), quantifying a specific WWTP's influence on irrigated fields in its discharge area. This involves multiplying each field's area by the proportion of emitted tracer reaching it, summing these values for all affected fields, and then dividing by the total affected area. The SII serves as a weighted measure, emphasizing both the quality of TWW in individual fields and the overall affected area within the discharge zone of the WWTP. Furthermore, the SII enables the ranking of WWTPs and identification of the most impactful for indirect reuse in agriculture. This information could support decisions on which WWTPs to prioritize for improvement (e.g., transitioning into Water Factories) based on their environmental impact.

How to cite: Rakonjac, N., Pronk, T., and P. Bartholomeus, R.: Spatial Correlation Between Treated Wastewater Quality and its De-facto Reuse in Agricultural Irrigation: A Case Study of the Netherlands, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13598, https://doi.org/10.5194/egusphere-egu24-13598, 2024.

Reservoirs play a pivotal role in mitigating floods and regulating river flows, particularly during irregular flows. In India, there are 6,138 large dams, yet only 119 are monitored by the Central Water Commission (CWC). In addition, the observations are available only from the year 2000 onwards. The long-term reservoir storage data are used in water management planning and flood control operations. It can also be used in calibrating hydrological models. However, long-term reservoir storage observations for the large dams in India are still lacking. We used hydrologic and hydrodynamic model framework to simulate the daily long-term reservoir storage data. We included more than 150 dams within the state-of-the-art Catchment-based Macroscale (CaMa) Flood model and generated the model simulated reservoir storage data. Further, we used the Long Short Term Memory (LSTM) algorithm to improve the model simulations using observations from CWC. We used the Global Reservoir Storage (GRS) data as observations for the dams not monitored by CWC. We intend to assess the application of the combined framework of the hydrological model and deep learning technique in simulating reservoir storage. Furthermore, we intend to analyse the long-term changes in the basin hydrology and the reservoir seasonal cycle. Long-term reservoir storage data can be utilized to plan water management and adaptation to climate change.

How to cite: Vegad, U. and Mishra, V.: Reconstructing Reservoir Storage of Indian Large Dams using Hydrological Model and Deep Learning Algorithms, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14729, https://doi.org/10.5194/egusphere-egu24-14729, 2024.

Human activities perturb the large-scale water cycle by withdrawing large amounts of freshwater for agriculture, manufacturing, energy production and drinking water supply, and by operating dams/reservoirs. The risk that human water demand exceeds freshwater availability widely threatens human water security and ecosystem health, in particular in the face of climate change. Therefore, national-scale hydrological models need to integrate representations of human activities to anticipate and address water scarcity and to support the design of adaptation strategies beyond the local scale. However, the lack of detailed observational datasets of human influence at a national scale hinders the development and evaluation of integrated modelling approaches.

This study focuses on processing a national observational dataset of human influence for hydrological modelling at the catchment scale in France, where climate change is expected to reduce water resources and increase water demand notably in the sector of irrigation. We collect data of water withdrawal, water release, reservoir operations from a large range of sources. These include national-scale datasets that are typically available at a coarse (annual) temporal resolution only and that are known to have large uncertainties, such as the French national database of quantitative water withdrawals. Covering a large spatial domain and attempting to account for uncertainties, our resulting dataset is a first step toward the development of robust integrated human-water system models at a national scale. 

How to cite: Sarrazin, F., Santos, L., Delaigue, O., Thirel, G., Andréassian, V., and Perrin, C.: Which data are available to evaluate the representation of human activities in hydrological models in France?, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16079, https://doi.org/10.5194/egusphere-egu24-16079, 2024.

Hydro-climatic diversity is a key challenge for national-scale water governance. Switzerland represents, with this respect, an extremely interesting case study: This small (40’000 km²) country encloses a wide range of hydro-climatic regimes (with precipitation ranging from 300 mm/year to > 2500 mm/year) and provides water to several large European rivers (Rhône, Rhine, Danube, Po). Over the years, an impressive number of hydrological models have been specifically developed or implemented for this country by national or regional authorities and by academia. The purpose of the models ranges from water resources and energy assessment to natural hazard management and real-time forecasting. Most existing models focus on the individual catchment scale, with few models extending to the regional and country scale. For real-time flood forecasting, there is a recent effort from the authorities to reduce the number of models; for national drought forecasting, a single model is used at the national scale, but for selected catchments, the simulations of 11 hydrological models are available for stakeholders. Existing climate change impact and water availability predictions (mostly developed at research institutes and universities) rely on collections of models applied at the individual catchment scale, with divergent results. Accordingly, despite the impressive amount of models being developed and implemented in this country, a coherent, national-scale strategy for hydrological modelling is missing; there is no general agreement on best practices among (academic) hydrologic modellers and the access of stakeholders to modelling results or modelling resources is heterogeneous. In this presentation, we discuss what we learned from the past, future challenges for national-scale hydrological modelling and possible ways forward.

How to cite: Schaefli, B., Horton, P., Kauzlaric, M., and Zappa, M.: Predicting water resources at Swiss national scale: the more models the better?, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19834, https://doi.org/10.5194/egusphere-egu24-19834, 2024.