Hydrological Surface Models Research Articles

Model simplification to simulate groundwater recharge from a perched gravel-bed river

Gravel-bed rivers are an important source of groundwater recharge in some regions of the world. Their interactions with groundwater are complex and highly variable in space and time, with considerable water storage in the riverbed sediments. In losing river sections, where most of the groundwater recharge occurs, the river can be separated from the regional groundwater system by an unsaturated zone (i.e., perched). The complexity of groundwater–surface water interactions in these environments calls for the use of 3D fully integrated hydrological models to represent them, but their computational intensity limits their practicality for parameter inference, uncertainty quantification and regional scale problems. On the other hand, the simple groundwater–surface water exchange functions currently implemented in regional scale groundwater models are not suited to represent complex gravel-bed river systems such as braided rivers. There is therefore a need for developing groundwater–surface water exchange functions tailored to gravel-bed rivers that can be used in regional scale models.To address this issue, we developed a model simplification framework that combines a 3D integrated surface and subsurface hydrological model, a 2D cross-sectional river-aquifer model and a 1D conductance-based analytical model. We aim at broadly simplifying the 3D model while ensuring the appropriate simulation of groundwater recharge. We demonstrate our modelling approach on the Selwyn River (New Zealand) using piezometric data and groundwater recharge estimates derived from field observations and satellite imagery.Our results indicates that groundwater recharge from this river can be simulated using a simple 1D analytical model, which can easily be implemented in regional groundwater models (e.g., MODFLOW models). However, to represent properly the time variability of groundwater recharge, it is essential to use the groundwater level in the shallow aquifer associated with the river as input to the regional groundwater model. Our approach is generally transferable to other gravel-bed rivers but requires some observations of river losses for proper calibration.

Assimilating multivariate remote sensing data into a fully coupled subsurface-land surface hydrological model

Hydrological models play a crucial role in tracking and predicting terrestrial water storage, yet they face challenges due to uncertainties and inaccuracies caused by various factors such as meteorological processes and data limitations. To refine these models, data assimilation has emerged as a valuable tool, utilizing a new source of data to update model states while considering associated uncertainties, thereby enhancing our comprehension and predictive capabilities in hydrological processes. In this context, satellite data are receiving increasing attention because they can cover large areas and are useful in detecting spatial and temporal variability of water.In most existing studies related to satellite data assimilation in hydrological models, one source satellite data is used in the analysis. This study focuses on improving subsurface water storage model accuracy by assimilating data from different satellite sources. In particular, we used data from the Soil Moisture and Ocean Salinity (SMOS) satellite and terrestrial water storage data from the Gravity Recovery and Climate Experiment (GRACE). The data are assimilated into a fully coupled subsurface-surface hydrological model, developed with ParFlow-CLM (PARallel FLOW- Community Land Model). The investigation is conducted in Iran. Employing an Ensemble Kalman Filter, three assimilation scenarios are explored: (i) GRACE, (ii) SMOS, and (iii) the combined assimilation of both GRACE and SMOS data (joint). Findings are validated against the Soil Moisture Active Passive (SMAP) and in-situ groundwater data by using a novel probabilistic reliability framework, demonstrating the advantages of joint data assimilation.The study highlights the influence of assimilated remote sensing data type on the effectiveness of data assimilation. Assimilating GRACE data enhances groundwater level estimations. However, SMOS data positively impacts topsoil moisture estimations, but adversely affects groundwater level estimates. Importantly, the assimilation of both GRACE and SMOS data through multivariate (joint) data assimilation significantly improves accuracy for both soil moisture estimation and groundwater level estimation.

Methodology for the assessment of poor-data water resources.

Surface hydrologic modeling becomes a problem when insufficient spatial and temporal information is available. It is common to have useful modeling periods of less than 15 years. The purpose of this work is to develop a methodology that allows the selection of meteorological and hydrometric stations that are suitable for modeling when information is scarce in the area. Based on the scarcity of data, a series of statistical tests are proposed to eliminate stations according to a decision-making process. Although the number of stations decreases drastically, the information used is reliable and of adequate quality, ensuring less uncertainty in the surface simulation models. Individual basin modeling can be carried out considering the poor data. The transfer of parameters can be applied through the nesting of basins to have information distributed over an extensive area. Therefore, temporally and spatially extended modeling can be achieved with information that preserves statistical parameters over time. If data management and validation is performed, the modeled watersheds are well represented; if this is not done, only 26% to 50% of the runoff is represented.

Learning Distributed Parameters of Land Surface Hydrologic Models Using a Generative Adversarial Network

AbstractLand surface hydrologic models adeptly capture crucial terrestrial processes with a high level of spatial detail. Typically, these models incorporate numerous uncertain, spatially varying parameters, the specification of which can profoundly impact the simulation capabilities. There is a longstanding tradition wherein parameter calibration has served as the conventional procedure to enhance model performance. However, calibrating distributed land surface hydrologic models presents a great challenge, often resulting in uneven spatial performance due to the compression of information inherent in model outputs and observations into a single‐value objective function. To address this problem, we propose a novel Generative Adversarial Network‐based Parameter Optimization (GAN‐PO) method. By leveraging a deep neural network to discern model spatial biases, we train a generative network to produce spatially coherent parameter fields, minimizing distinctions between simulations and observations. By leveraging neural network‐based surrogate models to make the physical model differentiable, we employ GAN‐PO to calibrate the Variable Infiltration Capacity (VIC) model against evapotranspiration (ET) over China's Huaihe basin. The results show that GAN‐PO can diminish errors in simulated ET derived from default parameters across nearly all grid cells within the study region, surpassing the conventional calibration approach based on the parameter regionalization technique. Ablation analysis indicates that relying solely on the traditional loss could lead to deteriorated model performance, underscoring the crucial role of the discriminator. Notably, due to the discriminator's explicit identification of model spatial biases, GAN‐PO excels in maintaining spatial consistency, outperforming the state‐of‐the‐art differentiable parameter learning (dPL) method in terms of model spatial performance.

HGS-PDAF (version 1.0): a modular data assimilation framework for an integrated surface and subsurface hydrological model

Abstract. This article describes a modular ensemble-based data assimilation (DA) system which is developed for an integrated surface–subsurface hydrological model. The software environment for DA is the Parallel Data Assimilation Framework (PDAF), which provides various assimilation algorithms like the ensemble Kalman filters, non-linear filters, 3D-Var and combinations among them. The integrated surface–subsurface hydrological model is HydroGeoSphere (HGS), a physically based modelling software for the simulation of surface and variably saturated subsurface flow, as well as heat and mass transport. The coupling and capabilities of the modular DA system are described and demonstrated using an idealised model of a geologically heterogeneous alluvial river–aquifer system with drinking water production via riverbank filtration. To demonstrate its modularity and adaptability, both single and multivariate assimilations of hydraulic head and soil moisture observations are demonstrated in combination with individual and joint updating of multiple simulated states (i.e. hydraulic heads and water saturation) and model parameters (i.e. hydraulic conductivity). With the integrated model and this modular DA framework, we have essentially developed the hydrologically and DA-wise robust toolbox for developing the basic model for operational management of coupled surface water–groundwater resources.

Unraveling phenological and stomatal responses to flash drought and implications for water and carbon budgets

Abstract. In recent years, extreme droughts in the United States have increased in frequency and severity, underlining a need to improve our understanding of vegetation resilience and adaptation. Flash droughts are extreme events marked by the rapid dry down of soils due to lack of precipitation, high temperatures, and dry air. These events are also associated with reduced preparation, response, and management time windows before and during drought, exacerbating their detrimental impacts on people and food systems. Improvements in actionable information for flash drought management are informed by atmospheric and land surface processes, including responses and feedbacks from vegetation. Phenologic state, or growth stage, is an important metric for modeling how vegetation modulates land–atmosphere interactions. Reduced stomatal conductance during drought leads to cascading effects on carbon and water fluxes. We investigate how uncertainty in vegetation phenology and stomatal regulation propagates through vegetation responses during drought and non-drought periods by coupling a land surface hydrology model to a predictive phenology model. We assess the role of vegetation in the partitioning of carbon, water, and energy fluxes during flash drought and carry out a comparison against drought and non-drought periods. We selected study sites in Kansas, USA, that were impacted by the flash drought of 2012 and that have AmeriFlux eddy covariance towers which provide ground observations to compare against model estimates. Results show that the compounding effects of reduced precipitation and high vapor pressure deficit (VPD) on vegetation distinguish flash drought from other drought and non-drought periods. High VPD during flash drought shuts down modeled stomatal conductance, resulting in rates of evapotranspiration (ET), gross primary productivity (GPP), and water use efficiency (WUE) that fall below those of average drought conditions. Model estimates of GPP and ET during flash drought decrease to rates similar to what is observed during the winter, indicating that plant function during drought periods is similar to that of dormant months. These results have implications for improving predictions of drought impacts on vegetation.

Ensemble Forecasts of Extreme Flood Events with Weather Forecasts, Land Surface Modeling and Deep Learning

Integrating numerical weather forecasts that provide ensemble precipitation forecasts, land surface hydrological modeling that resolves surface and subsurface hydrological processes, and artificial intelligence techniques that correct the forecast bias, known as the “meteo-hydro-AI” approach, has emerged as a popular flood forecast method. However, its performance during extreme flood events across different interval basins has received less attention. Here, we evaluated the meteo-hydro-AI approach for forecasting extreme flood events from headwater to downstream sub-basins in the Luo River basin during 2010–2017, with forecast lead times up to 7 days. The proposed meteo-hydro approach based on ECMWF weather forecasts and the Conjunctive Surface-Subsurface Process version 2 land surface model with a spatial resolution of 1 km captured the flood hydrographs quite well. Compared with the ensemble streamflow prediction (ESP) approach based on initial conditions, the meteo-hydro approach increased the Nash-Sutcliffe efficiency of streamflow forecasts at the three outlet stations by 0.27–0.82, decreased the root-mean-squared-error by 22–49%, and performed better in reliability and discrimination. The meteo-hydro-AI approach showed marginal improvement, which suggested further evaluations with larger samples of extreme flood events should be carried out. This study demonstrated the potential of the integrated meteo-hydro-AI approach for ensemble forecasting of extreme flood events.

The need to calibrate the surface hydrological model of Tenerife in the face of adverse meteorological phenomena such as the one that occurred on February 1, 2010, which produced heavy runoffs in Bco. de Santos.

The watershed of the Barranco de Santos is one of the largest on the island of Tenerife. The riverbed has its source at an altitude of 945 metres above sea level, on the Las Mercedes mountain, and flows through the Vega de La Laguna, descending towards the sea, bordering the Anaga mountains to the southwest and finally flowing into the Los Llanos dock in the port of Santa Cruz de Tenerife. Its route of more than 16 km has stretches where it becomes a real canyon, with cliff walls on its banks up to a hundred metres high. The average rainfall on the island of Tenerife is generally not very high, which means that the riverbed does not normally flow except in torrential episodes, which can cause unforeseen flooding, causing great material damage and even personal injury.\nDespite the large size of this basin, there are no flow gauges with which to estimate discharge volumes. This is mainly due to the fact that the episodes that produce runoff in the channel are usually sporadic and torrential. This document analyses the adverse phenomenon that occurred on 1 February 2010 and which gave rise to heavy runoff in the Barranco de Santos basin.

Exploring the Drivers for Changes in Lake Area in a Typical Arid Region during Past Decades

Lakes are important surface water bodies, and ongoing climate change is a growing threat to the hydrological cycle and water resource availability of lakes in arid regions. Accurately estimating different drivers’ contributions to lake water volume can enhance our understanding of lake variations in arid regions. In this study, we combined the land surface model and hydrological model, as well as statistical methods, to analyze the spatiotemporal heterogeneity of lake area changes and the factors affecting these changes during the past decades in Bosten Lake, Ulungur Lake, Ebinur Lake, and Sayram Lake, which are located in a typical dry region in China. The study revealed that the average amounts of river inflow, TWVF, lake ice sublimation, lake surface precipitation, and river outflow in the four lakes were 17.41 × 108 m3 yr−1, 6.60 × 108 m3 yr−1, 0.41 × 108 m3 yr−1, 0.98 × 108 m3 yr−1, and 9.12 × 108 m3 yr−1, respectively. We found that river inflow is the dominant factor affecting changes in open lake areas, while lake surface precipitation is the main factor affecting changes in closed lake areas. Our findings suggest that the main factors dominating the variability of lake water volume differ in different phases and lake types.

Towards Improved Flash Flood Forecasting over Dire Dawa, Ethiopia Using WRF-Hydro

Flash floods are increasingly frequent worldwide. Recent flooding in eastern Ethiopia resulted in casualties, destruction of property and interruptions of service. National flash flood forecasts made today primarily consider precipitation, putting less emphasis on surface processes. Enhancing accurate flash flood forecasts by accounting for surface processes and hydrological models together with a deeper understanding of heavy precipitation mechanisms is of paramount importance. To this end, an uncoupled WRF-Hydro model was calibrated for eastern Ethiopia to simulate extreme floods. Sensitivity analysis for August 2006 showed that infiltration runoff, hydraulic soil conductivity and saturated volumetric soil moisture with parameter values of 0.1, 1.5 and 1.0 produced realistic streamflow distribution. Extreme floods in March 2005 and April 2007 were further studied. The results showed that WRF-Hydro replicates temporal and spatial patterns well. Analysis using observational/reanalysis data revealed associated physical processes. Precipitation during these events exceeded long-term climatology and spanned wider areas in eastern Ethiopia. These heavy precipitation events are associated with strong upper-level westerly jet streams and rainfall-conducive circulation anomalies at lower levels. Positive outcomes from WRF-Hydro suggest operational implementation for flood monitoring and early warning systems in forecasting centers.

On Groundwater Recharge in Variably Saturated Subsurface Flow Models

AbstractGroundwater models that simulate only saturated flow use groundwater recharge as an input parameter. In contrast, variably saturated subsurface flow models, including integrated surface and subsurface hydrologic models, can jointly simulate the movement of water in the saturated and unsaturated zones. Instead of recharge, they require climate data such as precipitation and potential evapotranspiration. Given that the latter models represent hydrological processes operating throughout the unsaturated zone and at the water table, one might expect that recharge can be readily extracted from them. In this paper, we demonstrate that it is not the case. When the commonly used definitions of groundwater recharge are implemented in variably saturated subsurface flow models, they do not yield meaningful results. Above all, the problems occur because of the storage dynamics in the capillary fringe above the water table. Despite this difficulty, variably saturated subsurface flow models can provide the information required for water resources management directly.

Cycles‐L: A Coupled, 3‐D, Land Surface, Hydrologic, and Agroecosystem Landscape Model

AbstractManaging landscapes to increase agricultural productivity and environmental stewardship can be informed by spatially‐distributed models that operate at spatial and temporal scales that are intervention‐relevant. This paper presents Cycles‐L, a landscape‐scale agroecosystem and hydrologic modeling system, using as a test case a watershed in Pennsylvania. Cycles‐L emerges from melding the landscape and hydrology structure of Flux‐PIHM, a 3‐D land surface hydrologic model, with the agroecosystem processes in the Cycles model. Consequently, Cycles‐L can simulate processes affected by topography, soil heterogeneity, and management practices, owing to its physically‐based hydrology that can simulate horizontal and vertical transport of solutes with water. The model was tested at a 730‐ha experimental watershed within the Mahantango Creek watershed. Cycles‐L simulated well stream water and mineral nitrogen discharge (Nash‐Sutcliffe coefficient 0.55 and 0.60, respectively) and grain yield (root mean square error 1.2 Mg ha−1). Cycles‐L outputs are as good or better than those obtained with the uncoupled Flux‐PIHM (water discharge) and Cycles (grain yield) models. Modeled spatial patterns of nitrogen fluxes like denitrification illustrate the combined control of crop management and topography. For example, denitrification is almost twice as high when simulated with Cycles‐L than when simulated with Cycles 1‐D. Due to its spatial and temporal resolution, Cycles‐L fills a gap in the availability of models that operate at a scale relevant to evaluate interventions in the landscape. Cycles‐L can become a central component in tools for climate change scenario analysis, precision agriculture, precision conservation, and artificial intelligence‐based decision support systems.

Data space inversion for efficient uncertainty quantification using an integrated surface and sub-surface hydrologic model

Abstract. It is incumbent on decision-support hydrological modelling to make predictions of uncertain quantities in a decision-support context. In implementing decision-support modelling, data assimilation and uncertainty quantification are often the most difficult and time-consuming tasks. This is because the imposition of history-matching constraints on model parameters usually requires a large number of model runs. Data space inversion (DSI) provides a highly model-run-efficient method for predictive uncertainty quantification. It does this by evaluating covariances between model outputs used for history matching (e.g. hydraulic heads) and model predictions based on model runs that sample the prior parameter probability distribution. By directly focusing on the relationship between model outputs under historical conditions and predictions of system behaviour under future conditions, DSI avoids the need to estimate or adjust model parameters. This is advantageous when using integrated surface and sub-surface hydrologic models (ISSHMs) because these models are associated with long run times, numerical instability and ideally complex parameterization schemes that are designed to respect geological realism. This paper demonstrates that DSI provides a robust and efficient means of quantifying the uncertainties of complex model predictions. At the same time, DSI provides a basis for complementary linear analysis that allows the worth of available observations to be explored, as well as of observations which are yet to be acquired. This allows for the design of highly efficient, future data acquisition campaigns. DSI is applied in conjunction with an ISSHM representing a synthetic but realistic river–aquifer system. Predictions of interest are fast travel times and surface water infiltration. Linear and non-linear estimates of predictive uncertainty based on DSI are validated against a more traditional uncertainty quantification which requires the adjustment of a large number of parameters. A DSI-generated surrogate model is then used to investigate the effectiveness and efficiency of existing and possible future monitoring networks. The example demonstrates the benefits of using DSI in conjunction with a complex numerical model to quantify predictive uncertainty and support data worth analysis in complex hydrogeological environments.

A surrogate modeling method for distributed land surface hydrological models based on deep learning

Surrogate modelling methods have been used to estimate spatially distributed parameters of land surface hydrological models due to their computational efficiency. However, traditional surrogate techniques have trouble in modelling high-dimensional input–output maps. In addition, their applications to a distributed model over large spatial and temporal domains are very unlikely to produce consistent spatial pattern and dynamic relationship of model input and output, leading to unbalanced model performance across different spatiotemporal domains. To address these issues, we employ a deep autoregressive neural network to construct an accurate and reliable surrogate system of distributed dynamic model outputs. The network uses a deep convolutional encoder-decoder architecture to take full advantage of the deep convolutional layers in high-dimensional image processing and spatial feature extraction. Moreover, the autoregressive strategy makes the network good at handling dynamic relationship between the time-varying model input and output. We apply this deep learning (DL) network to building a surrogate model of the Variable Infiltration Capacity (VIC) model simulated soil moisture over the Huaihe river basin of China. The results show that the adopted deep autoregressive neural network can provide an accurate surrogate model of the high-dimensional dynamic relationship between 15 input fields and 1 output field, each covering 408 grids, over multiple years. This surrogate model significantly outperforms the popular long short-term memory (LSTM) model, achieving an average mean squared error (MSE) of 0.26 and an average R2 of 0.76 using the test datasets. The surrogate's MSE and R2 performance represents an average improvement of 43% and 33%, respectively, compared to the LSTM model. In addition, it shows better spatial performance than the LSTM model, demonstrating its superior performance in approximating spatial patterns of VIC simulated soil moisture. The accuracy of this surrogate method and its ability to handle high-dimensional data is promising for advancing parameter uncertainty quantification of distributed land surface hydrological models, which usually have high dimensionality due to the large number of variables involved, including distributed parameters, input forcing variables, and output variables.

A Transformer-Based Framework for Parameter Learning of a Land Surface Hydrological Process Model

The effective representation of land surface hydrological models strongly relies on spatially varying parameters that require calibration. Well-calibrated physical models can effectively propagate observed information to unobserved variables, but traditional calibration methods often result in nonunique solutions. In this paper, we propose a hydrological parameter calibration training framework consisting of a transformer-based parameter learning model (ParaFormer) and a surrogate model based on LSTM. On the one hand, ParaFormer utilizes self-attention mechanisms to learn a global mapping from observed data to the parameters to be calibrated, which captures spatial correlations. On the other hand, the surrogate model takes the calibrated parameters as inputs and simulates the observable variables, such as soil moisture, overcoming the challenges of directly combining complex hydrological models with a deep learning (DL) platform in a hybrid training scheme. Using the variable infiltration capacity model as the reference, we test the performance of ParaFormer on datasets of different resolutions. The results demonstrate that, in predicting soil moisture and transferring calibrated parameters in the task of evapotranspiration prediction, ParaFormer learns more effective and robust parameter mapping patterns compared to traditional and state-of-the-art DL-based parameter calibration methods.

Human activities reshape the drought regime in the Yangtze River Basin: a land surface-hydrological modelling analysis with representations of dam operation and human water use

Human activities reshape the drought regime in the Yangtze River Basin: a land surface-hydrological modelling analysis with representations of dam operation and human water use

Simulating the recession dynamics of Arctic catchments in the context of a thawing permafrost

In cold regions, climate warming is causing permafrost to thaw, which modifies the dynamics of groundwater flow from a local system, constrained by frozen ground, to a regional system of interconnected aquifers. Under the assumption presented in Brutsaert (2005), the recession slope of an arctic catchment hydrograph is linearly related to permafrost thawing depth. The recession analysis of arctic river flow may therefore reflect permafrost thawing dynamics. In areas where permafrost observations are rare, recession analysis appears as a valuable method to be exploited since there are extensive datasets of river-discharge for the Arctic, with an exceptionally large temporal and spatial coverage. Yet, it has been shown that the linear relationship between recession slope and permafrost thaw depth may be complicated by the extent of the permafrost, the landscape topography as well as the hydraulic properties of the aquifer. The integrated surface and subsurface hydrologic model Hydrogeosphere (HGS) is used here to account for hydrological complexifications and mechanisms involved when permafrost extent decreases and landscape hillslope increases. Previous modeling studies have already tested the impacts of these factors on the hydrological signature of arctic catchments. However, few have been able to (1) simulate permafrost extent, catchment hillslope and the resulting groundwater flowpaths in three dimensions, (2) distinguish the impact of permafrost thawing in extent from those of permafrost thawing in thickness and (3) represent the connection between surface and subsurface with a coupled approach. These assets allow to conclude that river-tàlik development and valley incision both clearly increase the vertical connectivity of the subsurface flowpaths as well as the non-linearity of the reservoir. The flexibility and the realism of HGS model allow to compare the modeling outputs with the dynamics of 336 real catchments from the Arctic. The comparison corroborates our results: below continuous, assumptions behind Brutsaert conceptual model are not verified and it is no longer possible to relate recession constant to permafrost thaw depth. Finally, this study gives the keys to start elaborating on the recession analysis method to be able to relate the hydrological signature of Arctic rivers to permafrost thawing rate in any type of permafrost extent and catchment topography.

The Impact of Inter-Basin Water Transfer Schemes on Hydropower Generation in the Upper Reaches of the Yangtze River during Extreme Drought Years

The Yangtze River Basin experiences frequent extreme heatwaves and prolonged droughts, resulting in a tight supply demand balance of electricity and negatively impacting socioeconomic production. Meanwhile, ongoing inter-basin water diversion projects are planned that will divert approximately 25.263 billion cubic meters of water from the Yangtze River Basin annually, which may further affect the power supply in the region. In this study, the CLHMS-LSTM model, a land-surface hydrological model coupled with a long short-term memory (LSTM)-based reservoir operation simulation model, is used to investigate the impact of water diversions on the power generation of the Yangtze River mainstream reservoirs under extreme drought conditions. Two different water diversion schemes are adopted in this study, namely the minimum water deficit scheme (Scheme 1) and minimum construction cost scheme (Scheme 2). The results show that the land surface–hydrological model was able to well characterize the hydrological characteristics of the Yangtze River mainstem, with a daily scale determination coefficient greater than 0.85. The LSTM reservoir operation simulation model was able to simulate the reservoir releases well, with the determination coefficient greater than 0.93. The operation of the water diversion projects will result in a reduction in the power generation of the Yangtze River mainstem by 14.97 billion kilowatt-hours. As compared to the minimum construction cost scheme (Scheme 2), the minimum water deficit scheme (Scheme 1) reduces the loss of power generation by 1.38 billion kilowatt-hours. The research results provide new ideas for the decision-making process for the inter-basin water diversion project and the formulation of water diversion plans, which has implications for ensuring the security of the power supply in the water diversion area.

Stakeholder-Informed Hydroclimate Scenario Modeling in the Lower Santa Cruz River Basin for Water Resource Management

The Lower Santa Cruz River Basin Study (LSCRB Study) is a collaborative effort of regional and statewide water management stakeholders working with the US Bureau of Reclamation under the auspices of the 2009 SECURE Water Act. The impacts of climate change, land use, and population growth on projected water supply in the LSCRB were evaluated to (1) identify projected water supply and demand imbalances and (2) develop adaptation strategies to proactively respond over the next 40 years. A multi-step hydroclimate modeling and risk assessment process was conducted to assess a range of futures in terms of temperature, precipitation, runoff, soil moisture, and evapotranspiration, with a particular focus on implications for ecosystem health. Key hydroclimate modeling process decisions were informed by ongoing multi-stakeholder engagement. To incorporate the region’s highly variable precipitation pattern, the study used a numerical “weather generator” to develop ensembles of precipitation and temperature time series for input to surface hydrology modeling efforts. Hydroclimate modeling outcomes consistently included increasing temperatures, and generated information related to precipitation responses (season length and timing, precipitation amount) considered useful for evaluating potential ecosystem impacts. A range of risks was identified using the hydroclimate modeling outputs that allowed for development of potential adaptation strategies.

Water availability identification of underground river in the Gunung Sewu karst area using inverse model

The karst area is identified in the Gunung Kidul Regency, the so-called Gunung Sewu karst area, as one of the Geopark areas in Indonesia. Gunungkidul Regency has had moderate to high yearly rainfall for the last ten years. However, this region has a problem with a surface water shortage. It is due to the characteristics of the karst, which allows rainwater to penetrate the geological layers accumulating in the certain noose yielding abundant underground water reserves. The purpose of this study is to assess the underground river water availability and the hydrological characteristics in the karst region using Inverse Modeling. Inverse Modeling is a method to identify the features of the underground river by adjusting the hydrological variables as an input and output discharge as a control. The FJ Mock Model as a surface hydrological model is used to characterize the Karst region’s underground river. There were five models in total to be simulated. The conditions under the Mock model show consistent results compared to the stepwise model in terms of low discharge conditions; Coefficient Correlation, RMSE, and R2 values obtain 0.637, 17.29, and 0.407, respectively. In this case, estimating the low flow conditions is more accurate than the high-rate flow conditions. The results are closer to the stepwise model without the infiltration variable involved.