Data-scarce Regions Research Articles

Automatic gap-filling of daily streamflow time series in data-scarce regions using a machine learning algorithm

Complete hydrological time series are crucial for water and energy resources management and modelling in a changing climate. The reliability of the non-parametric stochastic machine learning algorithm MissForest was assessed for gap-filling of daily streamflow time series in a data-scarce region with strong climatic variability. A total of 1,586 reconstructions of streamflows for 1970–2016 were analyzed. Overall, MissForest performed satisfactorily to well, allowing a precise and reliable simulation of the missing data quickly and automatically. MissForest performance increased with the number of predictor records and record length, achieving satisfactory results with 20 or more records having 15 or more years in length. Reconstructed daily streamflow time series of rivers with natural flow regimes were simulated with good performance, which slightly decreased for discharge magnitude alterations by runoff inputs from urbanized areas and water diversion for irrigation. In cases of severe alterations of the flow regime, such as by hydropeaking, MissForest failed at filling daily streamflow series gaps. Reconstructed hydrographs allow analysis of streamflow change and variability and their interactions with key climatic variables.

Development of Strategy for SWAT Hydrologic Modeling in Data-Scarce Regions of Peru

AbstractIn this study, methods were developed to create and evaluate the performance of the Soil and Water Assessment Tool (SWAT) in southern Peru where commonly used input data sources were not av...

A method to reconstruct flood scenarios using field interviews and hydrodynamic modelling: application to the 2017 Suleja and Tafa, Nigeria flood

The scarcity of model input and calibration data has limited efforts in reconstructing scenarios of past floods in many regions globally. Recently, the number of studies that use distributed post-flood observation data collected throughout flood-affected communities (e.g. face-to-face interviews) are increasing. However, a systematic method that applies such data for hydrodynamic modelling of past floods in locations without hydrological data is lacking. In this study, we developed a method for reconstructing plausible scenarios of past flood events in data-scarce regions by applying flood observation data collected through field interviews to a hydrodynamic model (CAESAR-Lisflood). We tested the method using 300 spatially distributed flood depths and duration data collected using questionnaires on five river reaches after the 2017 flood event in Suleja and Tafa region, Nigeria. A stepwise process that aims to minimize the error between modelled and observed flood depth and duration at the locations of interviewed households was implemented. Results from the reconstructed flood depth scenario produced an error of ± 0.61 m for all observed and modelled locations and lie in the range of error produced by studies using comparable hydrodynamic models. The study demonstrates the potential of utilizing interview data for hydrodynamic modelling applications in data-scarce regions to support regional flood risk assessment. Furthermore, the method can provide flow depths and durations at houses without observations, which is useful input data for physical vulnerability assessment to complement disaster risk reduction efforts.

Simulating the impact of extended desertification on Indian hydro climate using ICTP-RegCM4.4.5.10 model

Presented here is the numerical study of impact of extended desertification of the Thar desert (a data scarce arid region), on the Indian Summer Monsoon (ISM) meteorology using a robust state-of-the-art ICTP - regional climate model (RCM) RegCMv4.4.5.10 coupled with Biosphere – Atmosphere Transfer Scheme (BATS) land surface scheme. Firstly, the evolution of ISM precipitation, mean temperature and soil moisture during June to September 2001–2010 is verified using Emanuel over land and Grell over ocean with Arakawa and Schubert (1974) closure, in combination with the two planetary boundary layer (PBL) parameterization schemes, Holtslag and University of Washington - UW Turbulence closure model for PBL. Then the feedback due to extended desertification between land surface and PBL, during ISM regime is studied. The RCM simulations with Emanuel over land and Grell over ocean with Arakawa and Schubert (1974) closure in combination with UW PBL scheme, is able to properly simulate the rainfall, mean temperature JJAS climatology with bias in rainfall ~ 2 mm/day and bias in mean temperature is ~ 0.5–1 °C over the monsoon core region. Numerical experiments (control and design experiment) with the above mentioned combination of PBL schemes is performed for investigating the impact of extended desertification on the changes in the ISM precipitation, surface fluxes, circulation pattern, moisture fluxes and net radiation etc. In the extended desert design experiment, the land-use pattern in the model over Thar Desert (i.e. northwestern part of Indian sub-continent between Pakistan and northwestern India) is changed from “crop/mixed farming and semi-desert” to “desert/semi-desert” type of vegetation, mimicking extended desertification in the RegCMv4.4.5.10 model. Results show that due to increase in desertification over Thar deserts there is increase in albedo resulting from a decrease in plant cover, along with formation of anomalous anti-cyclonic circulation causes a decrease in JJAS rainfall (p < 0.01) over north, north-west India by −1 to −2 mm/day (~25% reduction). Anomalous anti-cyclonic circulation over the Indian land mass in the extended desertification experiment causes less convective heating and is responsible for the decrease in precipitation (with respect to control experiment). The anomalous anti-cyclonic circulation developed covers northern part of Bay of Bengal resulting in decrease in moisture advection of the order of −12 to −18 kgm-1sec-1 (~10% reduction). This drives away all moisture from the nearby regions. The effect of extended desertification induced feedbacks on variability and dominant modes of variability of Indian monsoon precipitation and soil wetness is also studied and reported. Finally, it is concluded that the relative magnitudes of the changes in albedo and vegetation (or vegetation roughness length) are important determinants of the size of climate change due to prescribed extended desert landscape over Thar desert region of Indian subcontinent. The results of this numerical study provides a basis and reference study for LULCC and mitigation measure human beings need to undertake for climate change adaptation.

Assessing data-scarce contaminated groundwater sites surrounding petrochemical industries

A common problem when studying groundwater contamination in low-income countries is that data required for a detailed risk assessment are limited. This study presents a method for assessment of the potential impact of groundwater contamination by total petroleum hydrocarbons (TPH) in a data-scarce region. Groundwater modeling, using the MODFLOW, was used to simulate regional-scale flow pattern. Then, a semi-analytical contamination transport model was calibrated by minimization of the absolute errors between measured and modeled concentrations. The method was applied to a case study in Kazakhstan to assess the potential spreading of a TPH plume, based on historical observations. The limited data included general information about the local geology, observations of GW level in the area, and concentrations during 5 years of TPH in monitoring wells surrounding the source of the pollution. The results show that the plume could spread up to 2–6 km from the source, depending on estimate of the initial concentrations, until the concentration reaches permissible levels. Sensitivity analysis identified parameters of longitudinal and transverse dynamic dispersivity together with the plume of TPH spreading, as the priority subjects for future investigations. The proposed approach can be used as a tool for governmental and municipal decision-makers to better plan the usage of affected groundwater sites in data-scarce regions. It can also help to decrease the negative impact of contaminated GW on human health and to better manage the industrial pollution.

From local to regional compound flood mapping with deep learning and data fusion techniques

Compound flooding (CF), as a result of oceanic, hydrological, meteorological and anthropogenic drivers, is often studied with hydrodynamic models that combine either successive or concurrent processes to simulate inundation dynamics. In recent years, convolutional neural networks (CNNs) and data fusion (DF) techniques have emerged as effective alternatives for post-flood mapping and supported current efforts of complex physical and dynamical modeling. Yet, those techniques have not been explored for large-scale (regional) compound flood mapping. Here, we evaluate the performance of a CNN & DF framework for generating CF maps along the southeast Atlantic coast of the U.S. as a result of Hurricane Matthew (October 2016). The framework fuses multispectral imagery from Landsat analysis ready data (ARD), dual-polarized synthetic aperture radar data (SAR), and coastal digital elevation models (DEMs) to produce flood maps at moderate (30 m) spatial resolution. The highest overall accuracy (97%) and f1-scores of permanent water/floodwater (99/100%) are achieved when ARD, SAR and DEM datasets are readily available and fused. Moreover, the resulting CF maps agree well (80%) with hindcast flood guidance maps of the Coastal Emergency Risk Assessment and can effectively match post-flood high water marks of the U.S. Geological Survey distributed in coastal counties. We ultimately evaluate the framework with different DF alternatives and highlight their usefulness for large-scale compound flood mapping as well as calibration of hydrodynamic models. The cost-effective approach proposed here enables efficient estimation of exposure to compound coastal flooding and is particularly useful in data scarce regions.

Watershed hydrological modelling in data scarce regions; integrating ecohydrology and regionalization for the southern Caspian Sea basin, Iran

The aim of this study is to evaluate an alternative approach to indicate how hydrological processes behave in a given watershed, and to test whether this approach can replace traditional calibration, particularly under data deficient conditions. Therefore, a regional calibration method (RC) was adapted to characterize “parameter-based hydrologic processes” as a function of watershed ecologic attributes. The methodological process included (1) temporal phase, (2) correlation analysis and (3) spatial phase. The defined methodology was carried out on a 4160 km2 area containing 21 watersheds laying in the southern coastal line of the Caspian Sea, Iran. By implementing the RC, regional models were specified corresponding to each hydrological process defined in the Tank model. Testing the reliability of the transferring process of hydrological parameters was conducted using multi-level accuracy comparison (MAC) benefiting from descriptive statistics, scatter-plots and T-test.Both temporal and spatial phases have shown acceptable outputs backed by their ecologic significance, but as an alternative approach to traditional calibration, the standalone RC still needs development to achieve a more robust basis covering all the parameters of the hydrologic model. According to the post-processor MAC, the transferability of six out of twelve regional models (height of lower outlet at the first tank, intermediate flow, deep-percolation, infiltration, surface flow, height of outlet at the second tank) was accepted with respect to the given tests. As such, our method outperformed the number of transferable parameters by an outstanding regional model predicting the surface flow in comparison with similar studies.Although the RC could not achieve total perfection, nevertheless it could still help users by providing more information about the contribution of ecologic variables in the prediction of the hydrological processes of a certain watershed.

Bare‐Earth DEM Generation in Urban Areas for Flood Inundation Simulation Using Global Digital Elevation Models

AbstractAccurate terrain representation is critical to estimating flood risk in urban areas. However, all current global elevation data sets can be regarded as digital surface models in urban areas as they contain building artifacts that cause artificial blocking of flow pathways. By taking surveyed terrain and LIDAR data as “truth,” the vertical error in three popular global DEMs (SRTM 1″, MERIT DEM, and TDM90) was analyzed in six European cities and an Asian city, with RMSE found to be 2.32–5.98 m. To increase the utility of global DEM data for flood modeling, a Random Forest model was developed to correct building artifacts in the MERIT DEM using factors from widely available public datasets, including satellite night‐time lights, global population density, and OpenStreetMap buildings. The proposed correction reduced the vertical errors of MERIT by 15%–67%, despite not using data samples from the target city in training the model. When training data from the target city was included error reduction improved by between 57 and 76 percentage points. The resulting Urban Corrected MERIT DEM improved simulated inundation depth by 18% over original MERIT in a hydrodynamic model of flooding in the UK city of Carlisle, although it did not outperform TDM90 at this site. We conclude that the proposed method has the potential to generate a bare‐earth global DEM in urban areas with improved terrain representation, although in data scarce regions this requires more complete OpenStreetMap building information. In the future, the method should be applied to TDM90.

Spatiotemporal changes of terrestrial water storage and possible causes in the closed Qaidam Basin, China using GRACE and GRACE Follow-On data

Terrestrial water storage (TWS) is a crucial indicator of regional water balance and water resources changes. Due to limited hydrological observations, we combined the Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GRACE-FO) products using the Long Short-Term Memory (LSTM) neural network to monitor the TWS changes from April 2002 to March 2020 over the closed Qaidam Basin in northwest China and examined the impacts of climate and meteorological changes on TWS variations. The results indicated that the LSTM model, driven by the cumulative precipitation, temperature, and Global Land Data Assimilation System datasets, was reliable for use in reconstruction of the GRACE products in the closed basin. The TWS variations featured seasonal variation characteristics and a significant upward trend at internal-annual scales, which were tested via linear statistics and a modified Mann–Kendall method. The increasing trend is likely to remain strongly sustainable in the near future with a Hurst index over 0.75 in most regions. Moreover, the TWS oscillation has a periodicity and nonlinearity increase trend of 0.43 mm/month as observed using ensemble empirical mode decomposition analysis, and the TWS components (including snow water equivalent, soil moisture, and groundwater) demonstrate discordant increasing trends in the basin. Under climate change conditions, teleconnection factors have strong impacts on TWS variability, particularly for the Pacific Decadal Oscillation index with a significant negative correlation by cross wavelet transform technology. Nonetheless, the increase in TWS is primarily influenced by precipitation increases and is more sensitive to the accumulated precipitation in this region. In this study, the GRACE products in combination with GRACE-FO data may help us to better understand the spatiotemporal characterization of TWS in Qaidam Basin, which will provide an important support for the water resource management and ecological environment protection in such data-scarce regions.

Generation of an improved precipitation data set from multisource information over the Tibetan Plateau

AbstractPrecipitation over the Tibetan Plateau (TP) known as Asia’s water tower plays a critical role in regional water and energy cycles, largely affecting water availability for downstream countries. Rain gauges are indispensable in precipitation measurement, but are quite limited in the TP that features complex terrain and the harsh environment. Satellite and reanalysis precipitation products can provide complementary information for ground-based measurements, particularly over large poorly gauged areas. Here we optimally merged gauge, satellite, and reanalysis data by determining weights of various data sources using artificial neural networks (ANNs) and environmental variables including elevation, surface pressure, and wind speed. A Multi-Source Precipitation (MSP) data set was generated at a daily timescale and a spatial resolution of 0.1° across the TP for the 1998–2017 period. The correlation coefficient (CC) of daily precipitation between the MSP and gauge observations was highest (0.74) and the root mean squared error was the second lowest compared with four other satellite products, indicating the quality of the MSP and the effectiveness of the data merging approach. We further evaluated the hydrological utility of different precipitation products using a distributed hydrological model for the poorly gauged headwaters of the Yangtze and Yellow rivers in the TP. The MSP achieved the best Nash-Sutcliffe efficiency coefficient (over 0.8) and CC (over 0.9) for daily streamflow simulations during 2004–2014. In addition, the MSP performed best over the ungauged western TP based on multiple collocation evaluation. The merging method could be applicable to other data-scarce regions globally to provide high quality precipitation data for hydrological research.

Spatial and temporal evaluation of global rainfall products in a data-scarce region: The Dez Basin, Iran

The limitation in approachability to rainfall data sources with an appropriate spatial-temporal distribution is a signifi-cant challenge in different parts of the world. The development of general circulation models and mathematical algorithms has led to the generation of various rainfall products as new sources with the potential to overcome the shortage in data-scarce basins. In this study, the performance of the PERSIANN-CCS and CMORPH satellite-based rainfall product, as well as the ERA5 and ERA-Interim reanalysis, was evaluated based on detection skill and quantitative metrics in a daily, month-ly and seasonal time scales in the Dez basin located in the southwest of Iran. The basin has a wide topographic variation and scattered rain gauge stations. Overall results denote that the ERA5 dataset has the best performance in all statistic veri-fication than other rainfall products. Based on the daily evaluation of all rainfall products, the false alarm rate (FAR) is higher than 0.5, so none of the datasets could capture the temporal variability of rainfall occurrence. This study has covered the western parts of the Zagros steep slopes in which the topographic conditions have a significant effect on the activity of rainfall systems. On a monthly scale, the mean value of the correlation coefficient (CC) for ERA5, ERA-Interim, PER-SIANN-CCS, and CMORPH was equal to 0.86, 0.85, 0.51, 0.39, respectively. The results of seasonal evaluation suggested that all datasets have better rainfall estimation in autumn and winter, and the capability of all datasets dramatically de-creased in the spring. The current paper argues that the ERA5 reanalysis typically outperforms ERA-Interim and can be considered as a reliable rainfall source in the future hydrological investigation in the southwest of Iran.

Spatiotemporal drought monitoring using bottom-up precipitation dataset (SM2RAIN-ASCAT) over different regions of Iran.

Drought is a natural phenomenon that can significantly impacts on water resources studies, agricultural and environmental societies around the world, hence, accurate spatio-temporal monitoring of drought is very important. In this research, a comparative analysis of a newly developed precipitation dataset, SM2RAIN-ASCAT (which is based on bottom-up approach), with 40 ground-measured Iranian Meteorological Organization (IMO) precipitation data are performed to estimate the precipitation and monitor the drought events over diverse climate regions of Iran. The SPI index, as a widely used drought index, at the temporal resolution ranging from one month to one year is used to this aim, and the outputs are analyzed based on the statistical and categorical metrics. Results indicated that the highest correlation coefficient (CC) and lowest root mean square error (RMSE) between SM2RAIN-ASCAT and in situ observations are found at 10-day and monthly time scales. Analyzing both datasets using FAR and POD indices in the mid and long-term time scales indicated that the SM2RAIN-ASCAT has a good performance in detecting rainy days. This product overestimate the precipitation values in extra-arid regions, while in humid and per-humid climate areas it tends to underestimation. Moreover, assessing the reliability of this product for drought monitoring showed that the SPI at 1, 3 and 6month time scales are in good agreement with ground-based observations over different climate regions of Iran. At these temporal resolutions, the CC value between SPIs calculated based on in situ observations and SM2RAIN-ASCAT is higher than 0.7 in more than 75% of the meteorological stations. The efficiency of SM2RAIN-ASCAT in detecting drought periods in extra-arid and arid zones is relatively better than that of in humid and per-humid climates. In addition, the performance of this product for capturing wet periods in extra-arid to semi-arid regions is better than that of in Mediterranean and humid zones. Overall, the outcomes of this study demonstrated that SM2RAIN-ASCAT, despite poor performance in estimating precipitation in some regions, can be considered as a complementary to ground-gauge observations or an appropriate alternative dataset for drought analysis, especially in arid and semi-arid regions which include most parts of the world.

Satellite‐Based Drought Reporting on the Navajo Nation

AbstractIn data scarce regions of the world, co‐produced management tools that combine remote sensing, modeled, and in situ data provide the information needed to support ground‐based monitoring systems for improved water and natural resource management. On the Navajo Nation (N.N.), in the Four Corners of the southwestern United States, there is a need for data‐driven management of water resources as the region is prone to water scarcity and emergency drought declarations, which have become more common under a changing climate. The current tools used by the N.N. Department of Water Resources largely lack the spatial detail, consistent measurements, and political and administrative information specific to their needs for determining appropriate response actions. The Drought Severity Evaluation Tool (DSET), a cloud‐based web application that harnesses the capabilities of Google Earth Engine, is a user‐friendly tool that pairs remotely sensed, modeled, and in situ data on the N.N. The DSET computing capabilities include on‐the‐fly generation of regional to field scale maps, time series figures, and reporting metrics that can assist in drought emergency declarations and the assistance to local communities through the subsequent allocation of relief dollars across the N.N. to the regions that need it most.

Estimating floodwater depth using SAR-derived flood inundation maps and geomorphic model in kosi river basin (India)

Flooding is the most widespread and frequent natural disaster in developing countries. Until recent years, determining flood extent and inundation depth were undertaken using hydrodynamic models but have pertinence constraints in data-scarce regions. This has given the new potential to characterize floods (e.g., inundation, depth, duration) at a large-scale using a geomorphic approach. The SAR data was employed to derive flood extent and the 12.5 m resolution DEM-based geomorphic method was applied to determine inundation depth in flooded domains of Kosi River Basin (KRB) in North Bihar (India) to characterize 2017 floods. The total inundated area in flooded domains over KRB was estimated at 4,108.2 km2 (20.88%). Most of the area (2,750 km2, 14%) of flooded domains over land had a water depth of 0.1 to 1 m. The geomorphic approach is appropriate for characterizing floods over large-scale and data-sparse basins like KRB and afforded a new horizon for flood risk assessment on flood vulnerable areas.

Evaluation of Reanalysis Precipitation Data and Potential Bias Correction Methods for Use in Data-Scarce Areas

Data availability and accessibility often present challenges to resolving regional water management issues. One primary input essential to models and other tools used to inform policy decisions is daily precipitation. Since observed datasets are not always present or accessible, data from the Climate Forecast System Reanalysis (CFSR) have become a potential alternative. A comparison of CFSR precipitation data to available observed data from stations in the East African countries Kenya, Uganda, and Tanzania showed notable differences between the two datasets, particularly with respect to precipitation totals and number of days receiving rainfall. A sliding window bias correction approach evaluated using 3 methods with 8 different window length and timestep variations showed that empirical quantile mapping with a 30-day sliding window length and 1-day timestep achieved the best performance. A comparison of bias corrected CFSR precipitation data against observed data showed marked improvement in the similarity of the number of wet days and maximum daily rainfall between the two datasets. For precipitation totals, bias correction reduced underprediction errors by 32% and overprediction errors by 81%. Results indicate that bias-corrected CFSR precipitation data provides an improved basis for water resources applications in the study region. Methodologies and approaches are extendable to other data-scarce regions or areas where complete and consistent data are not easily accessible.

Expert-based versus data-driven flood damage models: A comparative evaluation for data-scarce regions

The knowledge about potential flood damage is a key issue for disaster risk reduction. However, the scarcity of empirical data has limited flood damage modeling in several regions. As a result, studies in data-scarce regions have mostly been restricted to either building exposure assessment or identification of vulnerability indicators without a further linkage to probable damage. As expert-based approaches do not require empirical damage data, they have a high potential for flood damage modeling in data-scarce regions. In this study, we carried out a comparative assessment between an expert-based and a data-driven approach. The expert-based approach systematically combines the vulnerability indicator method and synthetic what-if analysis based on the knowledge of regional experts. The data-driven approach integrates empirical flood damage data in the analysis applying a multivariate random forest model. Flood damage data, collected through interviews after two flood events in 2017 and 2019 at separate locations in Nigeria, were used to evaluate the performance of both methods based on developed damage grades. Results from both methods showed i) a predictive accuracy of 30% and 38% for the expert-based and data-driven approaches respectively, ii) that distance to channel, wall material, building condition, and building quality are significant regional damage drivers, and iii) comparable model performance can be achieved even with a reduced number of variables. Furthermore, the study demonstrated how experts are likely to underestimate damage at low water depths and how a difference in conformity to building standards can add to challenges in flood damage prediction.

On the runoff validation of ‘Global BROOK90’ automatic modeling framework

Abstract The recently presented Global BROOK90 automatic modeling framework combines a non-calibrated lumped hydrological model with ERA5 reanalysis data as the main driver, as well as with global elevation, land cover and soil datasets. The focus is to simulate the water fluxes within the soil–water–plant system of a single plot or of a small catchment especially in data-scarce regions. The comparison to runoff is an obvious choice for the validation of this approach. Thus, we choose for validation 190 small catchments (with a median size of 64 km2) with discharge observations available within a time period of 1979–2020 and located all over the globe. They represent a wide range of relief, land cover and soil types within all climate zones. The simulation performance was analyzed with standard skill-score criteria: Nash–Sutcliffe Efficiency, Kling–Gupta Efficiency, Kling–Gupta Efficiency Skill Score and Mean Absolute Error. Overall, the framework performed well (better than mean flow prediction) in more than 75% of the cases (KGESS &amp;gt; 0) and significantly better on a monthly rather than on a daily scale. Furthermore, it was found that Global BROOK90 outperforms GloFAS-ERA5 discharge reanalysis. Additionally, cluster analysis revealed that some of the catchment characteristics have a significant influence on the framework performance.

Enhancing the Application of Earth Observations for Improved Environmental Decision-Making Using the Early Warning eXplorer (EWX)

The mitigation of losses due to extreme climate events and long-term climate adaptation requires climate informed decision-making. In the past few decades, several remote sensing and modeled-based Earth observations (EOs) have been developed to provide an unprecedented global overview and routine monitoring of climate and its impacts on vegetation and hydrologic conditions, with the goal of supporting informed decision-making. However, their usage in decision-making is particularly limited in climate-risk vulnerable and in situ data-scarce regions such as sub-Saharan Africa, due to lack of access to EOs. Here, we describe the Early Warning eXplorer (EWX), which was developed to address this crucial limitation and facilitate the application of EOs in decision-making, particularly in the food and water-insecure regions of the world. First, the EWX's core framework, which includes (i) the Viewer, (ii) GeoEngine, and (iii) Support Applications, is described. Then, a comprehensive overview of the Viewer, which is a web-based interface used to access EOs, is provided. This includes a description of (i) the maps and associated features to access gridded EO data and anomalies for different temporal averaging periods, (ii) time series graphs and associated features to access EOs aggregated over polygons such as administrative boundaries, and (iii) commonly used EOs served by the EWX that provide assessments of climate and vegetation conditions. Next, examples are provided to demonstrate how EWX can be used to monitor development, progression, spatial extent, and severity of climate-driven extreme events to support timely decisions related to mitigation of food insecurity and flooding impacts. Finally, the value of a regional implementation of EWX at the Regional Centre for Mapping of Resources for Development (RCMRD) in Nairobi, Kenya, is highlighted. Regional implementation of the EWX facilitates access to regionally focused EOs and their availability at polygon boundaries most relevant to the local decision-makers. Similar instances of EWX implemented in other regions, especially those susceptible to food and water security, will likely further enhance the application of EOs for informed decision-making.

Impact of Land Use Change on Non-Point Source Pollution in a Semi-Arid Catchment under Rapid Urbanisation in Bolivia

Changes in pollution pressure exerted on the Rocha River in Bolivia from diffuse sources were assessed using potential non-point pollution indexes (PNPI) for 1997 and 2017. PNPI is a simple, low-effort, time- and resource-saving method suitable for data-scarce regions, as it works at catchment level with commonly available geographical data. Land use type (obtained by Landsat imagery classification), runoff (determined by runoff coefficient characterisation) and distance to river network (calculated at perpendicular distance) were each transformed into corresponding indicators to determine their relative importance in generating pollution. Weighted sum, a multi-criteria analysis tool in the GIS environment, was used to combine indicators with weighting values. Different weighting values were assigned to each of the indicators resulting in a set of six equations. The results showed that higher PNPI values corresponded to human settlements with high population density, higher runoff values and shorter distance to river network, while lower PNPI values corresponded to semi-natural land use type, lower runoff coefficient and longer distances to river. PNPI values were positively correlated with measured nitrate and phosphate concentrations at six sub-catchment outlets. The correlation was statistical significant for phosphate in 2017. Maps were produced to identify priority source areas that are more likely to generate pollution, which is important information for future management.

Mapping water ecosystem services: Evaluating InVEST model predictions in data scarce regions

Sustainable management of water ecosystem services requires reliable information to support decision making. We evaluate the performance of the InVEST Seasonal Water Yield Model (SWYM) against water monitoring records in 224 catchments in southern Chile. We run the SWYM in three years (1998, 2007 and 2013) to account for recent land-use change and climatic variations. We computed squared Pearson correlations between SWYM monthly quickflow predictions and streamflow observations and applied a generalized mixed‐effects model to evaluate annual estimations. Results show relatively low monthly correlations with marked latitudinal and temporal variations while annual estimates show a good match between observed and modeled values, especially for values under 1000 mm/year. Better predictions were observed in regions with high rainfall and in dry years while poorer predictions were found in snow dominated and drier regions. Our results improve SWYM performance and contribute to water supply and regulation decision-making, particularly in data scarce regions.