Ecohydrological Model Research Articles

Ecohydrological risk assessment model for the Pra River Basin using GIS and multi-criteria decision making

ABSTRACT The Pra River Basin in Ghana is a major source of ecological, cultural, and economic benefits. However, it has been experiencing degradation, and logistical constraints have limited the implementation of Integrated Water Resources Management. This study presents a GIS-based ecohydrological risk model that identifies ecohydrological units with the highest degradation risk. First, the PRB was divided into 25 eco-hydrological units. Second, seven drivers of degradation were identified and mapped. The drivers were reclassified into five categories with risk scores of 1–5, with 5 indicating higher risk scores. The variables were ranked using the Analytical Hierarchy Process, the degradation risk model was calculated using the weighted sum method, and the multi-criteria decision tool was used to identify the ecohydrological units with the highest degradation risk. Of the 25 ecohydrological units delineated, the Buama, Lower Offin, Ayensu, and Oda ecohydrological units were found to be at the highest risk of degradation due to high artisanal small-scale gold mining, high population density, low forest/vegetation cover, and high encroachment in waterways. The results can be used as a guide to prioritize management intervention, starting with the areas classified as high risk and moving down to those classified as moderate risk.

Soil Phosphorus Transport in Response to Climate Change at Mid‐High Latitudes Under Intensive Agriculture

ABSTRACTPhosphorus (P) is an important soil element for sustaining plant growth and the integrity of terrestrial ecosystems, and the soil P cycle is strongly influenced by climate change and agricultural activities. However, little is known about how soil P has evolved with climate change and intensive agriculture at mid‐high latitudes, where the soil P cycle is sensitive to climate change. To answer this question, an ecohydrological model (EcoHAT‐P) driven by remote sensing data was used in this study to calculate soil P concentration and loss and was calibrated and validated using 272 soil samples collected in the Sanjiang Plain, a typical mid‐high latitude region with a long history of strong agricultural activity. Soil P concentration and loss, and plant uptake of soil P, were analyzed for the years 2000–2019 and 2020–2040. The results showed that soil total P, soil P loss, and plant P uptake all increased under intensive agriculture. The soil P cycle at mid‐high latitudes was more sensitive to temperature than to precipitation. Increased temperature would increase soil P loss and plant P uptake by 93.94% and 8.16%, respectively, and soil legacy P from intensive agriculture would become the main source even if external P inputs were eliminated. The results highlight the evolution of soil P transport at mid‐high latitudes and clarify the response of soil P cycle to climate change under intensive agriculture.

Advancing streamflow prediction in data-scarce regions through vegetation-constrained distributed hybrid ecohydrological models

Hybrid models that combine deep learning with physical principles have recently shown significant promise in improving streamflow prediction in data-scarce regions, achieving generally greater accuracy and reliability than either method alone. However, existing hybrid hydrological models underutilize the extensive and readily available remote sensing data on vegetation, which plays a critical role in hydrologic processes. Incorporating vegetation information can better constrain model processes by accounting for the interactions and feedbacks between vegetation and hydrology, thereby enabling more robust predictions. In this context, we present a novel distributed hybrid ecohydrological model that integrates dynamic vegetation processes into a distributed hybrid hydrological model, capable of simultaneously simulating leaf area index (LAI) and streamflow through multi-task learning. Our results indicate that in scenarios with limited available streamflow data, the combined constraint of LAI spatiotemporal patterns and streamflow significantly enhances the model’s ability to generalize streamflow simulation across space and time compared to models trained solely with either LAI or streamflow. Even with the increased availability of streamflow data, models utilizing both LAI and streamflow continue to exhibit superior robustness in streamflow prediction. The distributed ecohydrological model, which employs multi-process coupling and multi-source data constraints, accurately simulates the target variables (i.e., streamflow and LAI) while reliably capturing dynamic changes in other ecohydrological processes, such as evapotranspiration. This capability highlights its potential for multi-process diagnostics. Overall, our approach offers a new perspective for streamflow prediction in data-scarce regions and represents an important step towards integrating observational constraints from multiple subsystems into hybrid Earth system modeling

An Evaluation of Future Climate Change Impacts on Key Elements of the Water–Carbon Cycle Using a Physics-Based Ecohydrological Model in Sanchuan River Basin, Loess Plateau

An Evaluation of Future Climate Change Impacts on Key Elements of the Water–Carbon Cycle Using a Physics-Based Ecohydrological Model in Sanchuan River Basin, Loess Plateau

Revisiting evapotranspiration inputs in eco-hydrological modeling for climate change assessment

Evapotranspiration (ET) is an essential variable linking hydrological and ecological processes and is typically modeled as a function of potential evapotranspiration and soil moisture in traditional hydrological models. However, commonly used empirical ET models typically do not recognize the underlying vegetation dynamics. This can have implications when models are extrapolated under future climate change. In this study, the traditional HYMOD-BVM (THV) eco-hydrological model is adopted as a benchmark model. A modified HYMOD-BVM (MHV) is developed using an actual ET substitute for the traditional PET input to consider vegetation dynamics under climate change. The THV and MHV models are compared to evaluate how streamflow (Q) and leaf area index (LAI) vary under future climate in the Florentine River (FR, energy-limited) catchment and Murray River (MR, water-limited) catchment in Australia. Six global climate models (GCMs) from the latest Coupled Model Intercomparison Project (CMIP6) are bias-corrected and employed in the calibrated THV and MHV models, and ensemble mean results are analyzed. Results suggest that the streamflow projected by the MHV model tends to be higher than that from the THV model, while LAI presents an opposite trend. The energy-limited catchment appears more susceptible to climate change whereas larger vegetation effects are seen in the water-limited catchment. Overall, our research highlights the effects of evapotranspiration on future climate change scenario analysis and prompts the need for the development of spatial and temporal continuously accurate ET models for both current and future climates.

High flow management as climate change adaptation for freshwater ecosystems in regulated rivers

ABSTRACT Rivers worldwide have been disconnected from their floodplains through anthropogenic modifications, leading to a reduction in ecosystem services and decline in condition. These issues will be exacerbated by climate change. We assessed the effectiveness of relaxing river operating constraints to provide managed high flows to improve freshwater ecosystem outcomes in the Goulburn River basin in Victoria, Australia. We used a simplified, integrated ecohydrological modelling framework to help reduce the decision space of options in multiple river reaches, to highlight key thresholds of ecological responses, and to assess the robustness of options to an uncertain future climate. Constraints relaxation was shown to provide overall ecological benefits in the current climate, and to result in adaptation benefits for freshwater ecosystems in drier future climates. Our approach offers a practical way to assess multiple potential intervention options to improve ecosystem outcomes and be robust to uncertain climate change for complex regulated river systems.

MPeat2D – a fully coupled mechanical–ecohydrological model of peatland development in two dimensions

Abstract. Higher-dimensional models of peatland development are required to analyse the influence of spatial heterogeneity and complex feedback mechanisms on peatland behaviour. However, the current models exclude the mechanical process that leads to uncertainties in simulating the spatial variability in the water table position, vegetation composition, and peat physical properties. Here, we propose MPeat2D, a peatland development model in two dimensions, which considers mechanical, ecological, and hydrological processes together with the essential feedback from spatial interactions. MPeat2D employs poroelasticity theory that couples fluid flow and solid deformation to model the influence of peat volume changes on peatland ecology and hydrology. To validate the poroelasticity formulation, the comparisons between numerical and analytical solutions of Mandel's problems for two-dimensional test cases are conducted. The application of MPeat2D is illustrated by simulating peatland growth over 5000 years above a flat and impermeable substrate with free-draining boundaries at the edges, using constant and variable climate. In both climatic scenarios, MPeat2D produces lateral variability in the water table depth, which results in the variation in the vegetation composition. Furthermore, the drop in the water table at the margin increases the compaction effect, leading to a higher value of bulk density and a lower value of active porosity and hydraulic conductivity. These spatial variations obtained from MPeat2D are consistent with the field observations, suggesting plausible outputs from the proposed model. By comparing the results of MPeat2D to a one-dimensional model and a two-dimensional model without the mechanical process, we argue that mechanical–ecohydrological feedbacks are important for analysing spatial heterogeneity, shape, carbon accumulation, and resilience of peatlands.

Modelling vegetation dynamics for future climates in Australian catchments: Comparison of a conceptual eco-hydrological modelling approach with a deep learning alternative

Dynamically simulating leaf area index assists in modelling the feedbacks between eco-hydrologic and climatic processes. The particular challenge for Australia is the prevalence of arid and semi-arid ecosystems where water availability plays a crucial role in vegetation productivity. To understand whether existing LAI models can capture plant dynamics under changing climates, we tested two competing models across Australia's different climate zones: a conceptual eco-hydrologic model that applies water use efficiency term to relate LAI to water uptake, and a deep learning approach. An initial virtual catchment experiment with deep learning showed that it only uses information from potential evapotranspiration. For future climates, the conceptual model captured a negative trend and increasing variance in LAI, which is plausible given projected rainfall changes, while deep learning did not. Our study demonstrated an example of ‘right answer for the wrong reasons’, and the importance of incorporating knowledge of water-carbon coupling for appropriate scenarios.

How to close the loop with citizen scientists to advance meaningful science

Citizen science yields increased scientific capacity in exchange for science literacy and promises of a more responsive science to society’s needs. Yet, citizen science projects are criticized for producing few scientific outputs and having exploitative relationships with the citizens who participate. In the eagerness to capture new data, scientists can fail to see the value of citizen scientists’ expertise beyond data generation and can forget to close the loop with outputs that benefit the public interest. Citizen scientists are experts in their local environments who, when asked, can improve scientific processes and products. To the degree that citizen scientists are relegated to data collection, we shortchange opportunities to advance science. Rather than merely critique, we present an evidence-based engagement approach for listening to citizen scientist participants and incorporating their input into science processes and products that can be retrofitted onto existing citizen science projects or integrated from a project’s inception. We offer this adaptable blueprint in four steps and illustrate this approach via a crowdsourced hydrology project on the Boyne River, USA. We show how engaging voices of citizen scientists at key points in the project improves both the products of science (a real-time ecohydrological model) and the process of conducting the science (adaptations to help improve data collection). Distinct from outreach or education, considering citizen scientists as an equally interesting site of inquiry can improve the practice and outputs of science.

Linking terrestrial biogeochemical processes and water ages to catchment water quality: A new Damköhler analysis based on coupled modeling of isotope tracers and nitrate dynamics

Catchment-scale nitrate dynamics involve complex coupling of hydrological transport and biogeochemical transformations, imposing challenges for source control of diffuse pollution. The Damköhler number (Da) offers a dimensionless dual-lens concept that integrates the timescales of exposure and processing, but quantifying both timescales in heterogeneous catchments remains methodologically challenging. Here, we propose a novel spatio-temporal framework for catchment-scale quantification of Da based on the ecohydrological modeling platform EcH2O-iso that coupled isotope-aided water age tracking and nitrate modeling. We examined Da variability of soil denitrification in the heterogeneous Selke catchment (456 km2, central Germany). Results showed that warm-season soil denitrification was of catchment-wide significance (Da >1), while its high spatial variations were co-determined by varying exposure times and removal efficiencies (e.g., channel-connected lowland areas are hotspots). Moreover, Da seasonally shifted from processing-dominance to transport-dominance during the wet-spring season (from >1 to <1), implying important linkages between summer terrestrial denitrification and subsequent winter river water quality. Under the prolonged 2018–2019 droughts, denitrification removal generally reduced, resulting in further accumulation in agricultural soils. Moreover, the space-time responses of Da variability indicated important implications for catchment water quality. The older water in lowland areas exhibited extra risks of groundwater contamination, whilst agricultural areas in the hydrologically responsive uplands became sensitive hotspots for export and river water pollution. Importantly, the lowland pixels intersecting river channels exhibited high removal efficiencies, as well as high resilience to the disturbances (wet-spring Da shifted to >1 under drought conditions). The proposed catchment-wide Da framework is implied by mechanistic modeling, which is transferable across various environmental conditions. This could shed light on understanding of catchment N processes, and thus providing site-specific implications of non-point source pollution controls.

Reviews and syntheses: A scoping review evaluating the potential application of ecohydrological models for northern peatland restoration

Abstract. Peatland restoration and rehabilitation action has become more widely acknowledged as a necessary response to mitigating climate change risks and improving global carbon storage. Peatland ecosystems require restoration time spans of the order of decades and, thus, cannot be dependent upon the shorter-term monitoring often carried out in research projects. Hydrological assessments using geospatial tools provide the basis for planning restoration works as well as analysing associated environmental influences. “Restoration” encompasses applications to pre-restoration and post-restoration scenarios for both bogs and fens, across a range of environmental impact fields. The aim of this scoping review is to identify, describe, and categorize current process-based modelling uses in peatlands in order to investigate the applicability and appropriateness of ecohydrological and/or hydrological models for northern peatland restoration. Two literature searches were conducted using the entire Web of Science database in September 2022 and August 2023. Of the final 211 papers included in the review, models and their applications were categorized according to this review's research interests in seven distinct categories aggregating the papers' research themes and model outputs. Restoration site context was added by identifying 229 unique study site locations from the full database, which were catalogued and analysed against raster data for the Köppen–Geiger climate classification scheme. A majority of northern peatland sites were in temperate oceanic zones or humid continental zones that experienced snow. Over one in five models from the full database of papers were unnamed and likely intended for single use. Key themes emerging from topics covered by papers in the database included the following: modelling restoration development from a bog growth perspective, the prioritization of modelling greenhouse gas (GHG) emissions dynamics as a part of policymaking, the importance of spatial connectivity within or alongside process-based models to represent heterogeneous systems, and the increased prevalence of remote sensing and machine learning techniques to predict restoration progress with little physical site intervention. Models are presented according to their application to peatlands or broader ecosystem and organized from most to least complex. This review provides valuable context for the application of ecohydrological models in determining strategies for peatland restoration and evaluating post-intervention development over time.

Model linkage to assess forest disturbance impacts on water quality: A wildfire case study using LANDIS(II)-VELMA

Wildfires in western US forests increased over the last two decades, resulting in elevated solid and nutrient loadings to streams, and occasionally threatening drinking water supplies. We demonstrated that a linked LANDIS (LANDscape DIsturbance and Succession)-VELMA (Visualizing Ecosystem Land Management Assessments) modeling approach can simulate wildland fire effects on water quality using the 2002 Colorado Hayman Fire. Utilizing LANDIS-II's forest landscape model to simulate forest composition and VELMA's eco-hydrologic model to simulate pre- and post-fire water quantity and quality, the best calibration performance yielded a Nash-Sutcliffe Efficiency (NSE) of 0.621 during 2000–2006 (most optimal annual - 0.921) in comparison to North American Land Data Assimilation System (NLDAS) runoff. Pre-fire modeled runoff, nitrate, and surface water temperature (SWT) correlated with observations. Simulated post-fire runoff (229%) and SWT (20.6%) were elevated relative to pre-fire, with nitrate concentrations 34 times greater than the aquatic life threshold (0.01 mg N/L).

Runoff and sediment effect of the soil-water conservation measures in a typical river basin of the Loess Plateau

Soil erosion and sediment transport processes, which are intimately connected with hydrological processes, strongly affect land surface morphology and earth system dynamics. Over the past 60 years, intensive Soil-Water Conservation (SWC) measures implemented on the Loess Plateau have significantly reduced sediment load and concurrently exacerbated runoff declines, while the quantitative evaluation of the effects of SWC measures remains challenging. This study develops a sediment module which simulates the hillslope and in-stream sediment processes, and then incorporates the module into a Geomorphology-Based Ecohydrological Model (GBEHM). Besides, the model also couples with an explicit representation of different types of SWC measures, including both Hillslope SWC (HSWC) and River-network SWC (RSWC) measures. The newly developed hydro-sediment model is then applied to the Kuye River basin, a typical river basin on the Loess Plateau, and used for quantifying the effects of SWC measures on river discharge and sediment load during 1982–2019. Compared with Period 1 (1982–2000), the river discharge decreased by 48.7% in Period 2 (2001–2019), while the sediment load experienced a more substantial decrease of 95.3% between the two periods. Based on the numerical experiment results, HSWC and RSWC contributed to 25.4% (58 million m3/yr) and 28.9% (66 million m3/yr) of the annual runoff decline, as well as 58.1% (36 million t/yr) and 33.9% (21 million t/yr) of the annual sediment load reduction between the two periods, respectively. Besides, HSWC plays an increasingly important role in sediment reduction in recent years due to check dam silting. The model developed in this research is useful to evaluate the multi-faceted effects of existing SWC measures and to help with future SWC planning and policymaking.

Climate and vegetation change impacts on future conterminous United States water yield

Future water availability is influenced by both climate and associated vegetation dynamics. This study coupled vegetation projections from a dynamic global vegetation model (MC2) with an ecohydrological model, Water Supply Stress Index (WaSSI), to predict water yield at the 8-digit Hydrologic Unit Code (HUC8) watershed level for the conterminous United States (CONUS) for the 21st century. We considered two contrasting warming scenarios (Representative Concentration Pathways 8.5 and 4.5) and accounted for simulation uncertainty by using a large ensemble of climate model outputs. The coupled model projects a decrease in water yield across much of CONUS, especially towards end-century (2080–2099) under RCP 8.5 (warmer scenario), reaching up to −30% at the regional level, relative to the 2008–2027 baseline period. Overall, the projected water yield reduction under RCP 8.5 is roughly twice as high as under RCP 4.5. Substantial changes in water yield for watersheds in the central and southeastern United States are expected by mid-century (2040–2059), reaching up to −40% (RCP 4.5) and −75% (RCP 8.5) at at the century’s end (2080–2099), relative to 2008–2027, respectively. Climate change, rather than vegetation change, strongly dominates the projected future changes in water yield, with contributions typically one order of magnitude higher. For a small number of watersheds, the effects of vegetation change can mitigate or exacerbate the effects of climate change on water yield. Our simulation results suggest a widespread increase in aridity and evaporative indices and a decrease in soil moisture, especially under RCP 8.5. Our integrated modeling results can inform policy makers and resource development planners quantitative information of future water availability.

Effects of Climatic Disturbance on the Trade-Off between the Vegetation Pattern and Water Balance Based on a Novel Model and Accurately Remotely Sensed Data in a Semiarid Basin

Vegetation is a natural link between the atmosphere, soil, and water, and it significantly influences hydrological processes in the context of climate change. Under global warming, vegetation greening significantly aggravates the water conflicts between vegetation water use and water resources in water bodies in arid and semiarid regions. This study established an improved eco-hydrological coupled model with related accurately remotely sensed hydrological data (precipitation and soil moisture levels taken every 3 j with multiply verification) on a large spatio-temporal scale to determine the optimal vegetation coverage (M*), which explored the trade-off relationship between the water supply, based on hydrological balance processes, and the water demand, based on vegetation transpiration under the impact of climate change, in a semiarid basin. Results showed that the average annual actual vegetation coverage (M) in the Hailar River Basin from 1982 to 2012 was 0.62, and that the average optimal vegetation coverage (M*) was 0.56. In 67.23% of the region, M* was lower than M, which aggravated the water stress problem in the Hailar River Basin. By identifying the sensitivity of M* to vegetation characteristics and meteorological parameters, relevant suggestions for vegetation-type planting were proposed. Additionally, we also analyzed the dynamic threshold of vegetation under different climatic conditions, and we found that M was lower than M* under only four of the twenty-eight climatic conditions considered (rainfall increase by 10%, 20%, and 30% with no change in temperature, and rainfall increase by 20% with a temperature increase of 1 °C), thereby meeting the system equilibrium state under the condition of sustainable development. This study revealed the dynamic relationship between vegetation and hydrological processes under the effects of climate change and provided reliable recommendations to support vegetation management and ecological restoration in river basins. The remote sensing data help us to extend the model in a semiarid basin due to its accuracy.

Ecohydrological investigation of cloud seeding effect on vegetation activity in the Boryeong Dam Basin, South Korea

Cloud seeding is well known to address water shortage problems caused by droughts by adding more precipitation and consequent runoff. Unlike previous studies, this study investigates another positive effect of cloud seeding on the activation of vegetation by integrating numerical cloud seeding simulations and processed-based modeling of various ecohydrological components. As the carbon cycle is closely related to the hydrological processes in ecosystems, we adopt the RHESSys ecohydrological modeling to synthetically simulate runoff and soil moisture along with primary productivity and vegetation respiration. Numerical simulations with and without cloud seeding are generated by the WRF-ARW model for the Boryeong Dam basin, one of the basins vulnerable to droughts, in 2021. The cloud seeding simulations of two cases are input into the RHESSys model to examine changes in hydrological and ecological components due to the added amount of precipitation. The results exhibit significant increases in annual precipitation (18 %) and runoff (22 %), and enhanced soil moisture stimulating the ecological components such as GPP and NPP, especially in spring. Cloud seeding can be considered to create optimal conditions for vegetation to absorb or sequester carbon from the atmosphere, thereby boosting vegetation growth. Additionally, the time-lagged correlations between cloud seeding and soil moisture, GPP, NPP, and respiration suggest that vegetation activity is highly dependent on antecedent 1–2 months occurrences of cloud seeding. This study implies that the cloud seeding effect on additional NPP can be considered as a countermeasure of the global average forest loss, which means that carbon emission rise in the global warming era can be partly alleviated by cloud seeding.

A spatio-temporal analysis of environmental fate and transport processes of pesticides and their transformation products in agricultural landscapes dominated by subsurface drainage with SWAT+

Pesticides are detected in surface water and groundwater, endangering the environment. In lowland regions with subsurface drainage systems, drained depressions become hotspots for transport of pesticides and their transformation products (TPs). This study focuses on detailed modelling of the degradation and transport of pesticides with different physico-chemical properties. The objective is to analyse complex hydrological transport processes, to understand the temporal and spatial dynamics of the degradation and transport of pesticides. The ecohydrological model SWAT+ simulates hydrological processes as well as agricultural management and pesticide degradation, and can therefore be used to develop pesticide loss reduction strategies. This study focuses on modelling of three pesticides (pendimethalin, diflufenican, and flufenacet), and two TPs, flufenacet-oxalic acid (FOA) and flufenacet sulfonic acid (FESA). The study area is a 100-hectare farmland in the northern German lowlands of Schleswig-Holstein that is characterized by an spacious drainage network of 6.3 km and managed according to common conventional agricultural practice.SWAT+ modelled streamflow with very good agreement between observed and simulated data during calibration and validation. Regarding pesticides, the model performance for highly mobile substances is better than for non-mobile pesticides. While the transport of the moderately to very mobile substances via tile drains played an important role in both wet and dry conditions, no transport via tile drains was modelled for the highly sorptive and non-mobile pendimethalin.In conclusion, the model can reliably represent the degradation of moderately to very mobile pesticides in small-scale tile drainage-dominated catchments, as well as surface runoff-induced peak loads. However, it has weaknesses in accounting for the subsurface transport of non-mobile substances, which can lead to an underestimation of the subsequent delivery after precipitation events and thus underestimates the total load.

A global dataset of terrestrial evapotranspiration and soil moisture dynamics from 1982 to 2020

Quantifying terrestrial evapotranspiration (ET) and soil moisture dynamics accurately is crucial for understanding the global water cycle and surface energy balance. We present a novel, long-term dataset of global ET and soil moisture derived from the newly developed Simple Terrestrial Hydrosphere model, version 2 (SiTHv2). This ecohydrological model, driven by multi-source satellite observations and hydrometeorological variables from reanalysis data, provides daily global ET-related estimates (e.g., total ET, plant transpiration, soil evaporation, intercepted evaporation) and three-layer soil moisture dynamics at a 0.1° spatial resolution. Validation with in-situ measurements and comparisons with mainstream global ET and soil moisture products demonstrate robust performance of SiTHv2 in both magnitude and temporal dynamics of ET and soil moisture at multiple scales. The comprehensive water path characterization in the SiTHv2 model makes this seamless dataset particularly valuable for studies requiring synchronized water budget and vegetation response to water constraints. With its long-term coverage and high spatiotemporal resolution, the SiTHv2-derived ET and soil moisture product will be suitable to support analyses related to the hydrologic cycle, drought assessment, and ecosystem health.

Modeling the impacts of oil palm plantations on water quantity and quality in the Kais River Watershed of Indonesia

Oil palm plantations can impact hydrological processes in many tropical watersheds. The rapid conversion of tropical rainforests for commercial operations in recent decades has been associated with water scarcity, flooding, and polluted rivers. However, this widespread and emerging issue is less studied and underreported due to limited data availability, modeling complexity, and the remote nature of these landscapes. Ecohydrologic modeling enables us to investigate changes in watershed conditions caused by large-scale land cover changes from plantations. This study examines the impact of oil plantations on water quantity and quality using the SWAT+ model in the Kais River Watershed, West Papua, Indonesia. The objective is to assess the hydrological changes concerning land cover conversion to oil palm plantations. Results show that establishing oil palm plantations increased surface runoff by 21 %, and sediment yields rose by 16.9 % compared to the baseline. There was also a significant increase of 78 % in mean annual total nitrogen and 144 % in total phosphorous after the plantations' establishment. The results show that forest conversion to oil palm plantations in the Kais River watershed is a primary driver of change in hydrological regimes, resulting in the deterioration of water quality. There is a need for conservation strategies to mitigate the impacts of significant landscape changes in watershed ecosystems.

Assessing impacts of alternative land use strategies on water partitioning, storage and ages in drought‐sensitive lowland catchments using tracer‐aided ecohydrological modelling

AbstractContinuing negative rainfall anomalies, coupled with climate change projections of increased drought severity and frequency, drive an urgent need to increase resilience and integration in land and water management strategies in many regions of the world. However, complex interactions between land cover change, ecohydrological partitioning and water availability are difficult to quantify, especially at different temporal and spatial scales. In conjunction with local stakeholders, we developed plausible, alternative land use scenarios (including forest diversification and agroforestry schemes) based on the existing four primary land use types (i.e., conifer and broadleaved forests, arable agriculture, and pasture) in a 66 km2 drought‐sensitive catchment in northern Germany. We used modelling to evaluate spatial and temporal changes in water flux partitioning, storage and ages. The spatially‐distributed, tracer‐aided ecohydrological model, EcH2O‐iso, calibrated using hydrometric, ecohydrological and isotopic data at daily time steps from 2007 to 2019 was used in this assessment. The results showed that replacing conifer forests with uneven‐aged mixed forests with younger broad‐leaved trees had the greatest potential for reducing total evapotranspiration and increasing groundwater recharge. For coniferous forests, a 50% increase in the proportion of broad‐leaved trees was projected to result in an 11% increase in groundwater recharge across the catchment. The mixed‐forest management alternatives also reduced groundwater turnover times, which would support more rapid recovery of soil moisture and groundwater stores following droughts. This study demonstrates that such an ecohydrological modelling approach has the potential to contribute useful science‐based evidence for policy makers allowing quantitative assessment of potential land use effects on water availability and effective communication with stakeholders.