dataesr/scientific-paragraphs-categorization huggingface dataset:
Validation Data To validate the Landsat-derived reservoir area estimates we used a dataset of reservoir extents that were digitised manually from Google Earth imagery. Google Earth provides high resolution (<15 m) images from multiple sources with most images in the collection are sourced from Digitial Globe's satellites (~2 m resolution). We derived the validation dataset by, first, randomly selecting 250 reservoirs from the 1200 identified in this study. Second, we digitised 347 reservoir extents corresponding to every date that imagery were available in the Google Earth historical imagery collection over the 250 reservoirs within the period May 2013 to October 2015. Google Earth images covering the entire reservoir extent were available for more than one month during this period at some reservoirs, and were not available for any month at other reservoirs.
Of the 347 digitised reservoir surface extents, 75 were excluded from subsequent analyses because: (i) they exceeded the neighbourhood search area in GEE's connectedPixelCount function, which is limited to 1024 pixels (92 ha); or (ii) they were smaller than 0. 09 ha, equivalent to one Landsat pixel; or (iii) there were masked pixels in the underlying Landsat imagery, and therefore no Landsat-derived extent estimates against which to compare the validation data. Of the 250 randomly selected reservoirs, no suitable imagery were available over 48 reservoirs, while all of the digitized surface extents associated with a further 31 reservoirs were part of the 75 extents excluded from the validation dataset for one of the aforementioned reasons. The final validation dataset therefore contained 272 reservoir extents, from imagery acquired in different months across three hydrological seasons over 171 reservoirs.
These validation extents ranged from 0. 09 ha to 72. 4 ha with a mean of 7. 1 ha and median of 2. 3 ha (lower QR: 0. 8, upper QR: 7. 1 ha), dispersed spatially and seasonally across the basin (Appendix A Figure  A1 ). As expected, the validation data were skewed towards dry months (October through March for most of the basin) when cloud-free images are more likely to be available in the Google Earth historical imagery collection. Reservoirs are smaller in dry months as water levels recede, which partly explain the high proportion of small reservoirs in the validation dataset. Because drying patterns can also depend on reservoir depth and catchment size, we calculated the catchment area for each reservoir and confirmed that the distribution of our validation dataset was representative of the distribution of basin-wide reservoir catchment sizes using the WaterWorld  [52]  zones of interest tool.
Accuracy Assessment To assess the accuracy of reservoir area estimates derived from each surface water map (GSW, NDWI, MNDWI1, and MNDWI2), we compared the Landsat-derived reservoir areal extents and equivalent volumes to those in the validation dataset for corresponding image dates. We used the difference to compute the mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE) of Landsat-derived reservoir area and volume estimates. We identified the optimal threshold for classifying reservoir water using NDWI, MNDWI1, or MNDWI2 as that which provides reservoir area estimates with the lowest mean area percentage error.
Analysis of Environmental Covariates Accurate mapping of reservoir extents from Landsat imagery can be hindered by the methodological approach as well as environmental factors. Research on environmental sources of error in water classifications from Landsat data shows that green-brown water, arising for example because of suspended sediment or high chlorophyll content, reduces classification accuracy  [53]. Our validation data were based on images from multiple sources available in Google Earth (e. g. , WorldView, IKONOS, GeoEye, SPOT), making it challenging to visibly determine water colours of reservoirs in our validation dataset in a consistent manner. However, the Normalised Difference Vegetation Index (NDVI)  [54]  is sensitive to fractional green vegetation cover  [55]  and may be a suitable indicator of surface or sub-surface vegetation.
1) from applying NDWI, MNDWI1, and MNDWI2. We used percentage errors in reservoir area estimates as the dependent variable, and indicators of reservoir surface vegetation, reservoir geometry, and rainfall patterns as independent variables. In particular, as an indicator of reservoir surface vegetation, we used the mean NDVI from all pixels intersecting the reservoir extent, calculated in GEE from Landsat 8 OLI Surface Reflectance images acquired in the month each reservoir area estimate is made. We used reservoir area in hectares and reservoir perimeter-area ratio as recorded in our validation dataset, as indicators of reservoir size and shape. We used month and latitude as indicators of rainfall patterns. We selected the Random Forest approach  [56]  as a statistical method for checking the relationship between a dependent and multiple independent variables, which accepts categorical and continuous data as well as correlated variables (such as reservoir size and perimeter-area ratio) as inputs.
Random forest regression works by splitting the dataset into 63% test (bagged) data and 37% validation (out of bag) data, and then constructing multiple trees from random samples of the bagged data, such that the tree nodes represent decreasingly good predictors of the response variable  [57]. Residual errors for each estimate are computed by comparing the predicted response against actual response for out of bag data. The trees are combined into a single tree whose nodes are ordered according to the importance of each variable in predicting the response. The "Importance" is a measure of prediction error that is divided by the standard error. Results from the Random Forest were used to compare which variables were associated with errors in areal estimates, irrespective of the water classification method.
Next, we estimated the current maximum capacity for 1117 of the 1200 Volta basin reservoirs by identifying the largest extent recorded at these reservoirs between 1984 and 2015. We used one of the GSW derivative layers, the GSW Maximum Extent (GSW-MX), to get the maximum extent for the remaining 83 reservoirs whose maxima derived from GSW were ≥(92 ha-RMSE of area estimates), and therefore cannot be measured using our GEE approach, which is restricted to extents of less than 1024 pixels (~92 ha). The GSW-MX dataset highlights all of the pixels that have contained water at any time between 1984 and 2015. It was not available through GEE at the time of this analysis and so we extracted estimates from the GSW-MX in a desktop GIS. We assumed that reservoirs where no water was ever recorded are smaller than the minimum areal unit that can be reliably mapped using GSW data.
Reservoirs with data missing for any month outside this period were excluded from the analysis, as were reservoirs with no water ever recorded during the 32 years covered by the dataset. The monthly rate of water loss can be obtained from GSW-derived reservoir area estimates by subtracting the annual minima from the maxima, and dividing this by the number of months between the two extremes. Where the monthly water loss was less than the uncertainty contained in the reservoir area estimates, as estimated by the RMSE, we assumed the estimates cannot be reliably used to monitor the loss in reservoir water through the year and classified its seasonality as "uncertain". GEE codes that were used in our analyses are available here: https://earthengine. googlesource. com/SurfaceWaterMapping. R codes used to calculate estimation errors and the run random forest analyses are available on request.
Accuracy of Reservoir Area Estimates Comparing reservoir area estimates from GSW, NDWI, MDNWI1, and MNDWI2 against those in our validation dataset, we find that estimates varied sustainably in accuracy across methods. Careful selection of the threshold for water classification is critical for minimizing percentage errors in reservoir area estimates derived from NDWI, MNDWI1, and MNDWI2. The lowest MAPE was achieved using a threshold of -0. 2 on both the NDWI and MNDWI1, and a 0 threshold on MNDWI2 (see Appendix C Table  A1  for comparative accuracies of NDWI, MNDWI1, and MNDWI2 across all thresholds). We subsequently refer to results from NDWI, MNDWI1 and MNDWI2 corresponding to these optimal thresholds. Comparison of absolute errors in reservoir area estimates indicates MNDWI1 produced slightly better estimates than the other three approaches.
In other words, reservoir area estimates that were derived from the GSW dataset using our method were 19% less accurate that those that can be derived by applying MNDWI1 to Landsat 8 OLI imagery, resulting in 17% less accurate reservoir volume estimates (see Table  1 ). The additional overall inaccuracy of the GSW method when compared to MNDWI1 was associated with a higher number of omission errors, i. e. , 100% under-estimates (Table  2 ). In total 140 (51%) reservoir areas were omitted using the GSW-derived estimates, as compared to 82 using those from MNDWI1. These 100% GSW-derived underestimates occurred over very small reservoirs: 75% were smaller than 2. 9 ha (~21,788 m 3 ), and the median reservoir area was 1. 0 ha. In contrast, 100% underestimates in MNDWI1-derived reservoir areas occurred on reservoirs with a median area of 0.
Applications of Reservoir Extent Data in Agricultural Landscapes In total, 380 months of data are embedded in the March 1984 to October 2015 GSW dataset. However, there were substantial gaps in coverage over specific reservoirs and time periods in our derivative reservoir extent layer. Individual reservoirs had between 47% and 87% missing data across the 380 months; or, 74-98% pre-2000 and 18-77% from 2000 onwards. Gaps were concentrated in the period 1984-1999, indicative of gaps in the underlying USGS Landsat image archive  [58]  specifically over West and north Africa  [25]. Data availability increased with latitude, likely being a result of decreasing cloud cover. Data gaps of over 50% were present in most years during peak rainfall months, from June to September, when cloud-cover may prevent accurate water classification.
This may be because the difference in the amount of infrared light reflected from pure water and from vegetation is small relative to the difference in short-wave infrared light reflected  [27] , and crops and other waterline vegetation are prevalent around dryland reservoirs. The results further indicate that bands with shorter wavelengths slightly out-perform those with longer as short-wave infrared inputs to the MNDWI for mapping surface water extents from Landsat satellite imagery, as consistent with  [14, 29]. The higher uncertainty in GSW-derived area estimates indicates that the water classification algorithm used in creating the GSW datasets is less effective at distinguishing water from non-water pixels than NDWI or MNDWI using Landsat 8 OLI imagery, for our West African study site.
We show that reservoirs can be mapped manually through freely available Google Earth imagery-which could be completed using crowd-sourcing techniques (for example, see http://geodata. policysupport. org/ geowiki-databases)-allowing for reservoir capture over large spatial extents. As well as reducing gaps in reservoir inventories, mapping reservoirs prior to analysis of surface water maps enables application of simple waterbody extraction algorithms, such as the connectedPixelCount in GEE, significantly reducing the time required to extract data over large spatio-temporal extents. Using a server-based approach also avoids the significant computer storage and processing requirements that create challenges to analyses of multiple Landsat images  [16, 24, 39]. GEE is particularly useful for the analysis of many Landsat images, since each Landsat image is ~1 GB in size with a footprint of approximately 185 km 2 and collected on a 16-day time step, so temporal analyses over large areas require hundreds of gigabytes of imagery.
Access to Landsat satellite imagery, GSW datasets, and image processing algorithms through server-based tools such as GEE, once the software learning curve is surpassed, significantly reduces the time and cost that is involved in deriving and comparing the accuracy of waterbody extents across time and space. Data gaps in the reservoir area estimates from the GSW Monthly Histories dataset over the Volta basin limit the types of information that can be gathered from this resource. Most of these gaps are caused by missing imagery in the underlying Landsat archive  [25] , and therefore would not be avoided by applying an alternative water classification method on Landsat satellite imagery. Other gaps represent areas that are classified as non-valid by  [16] , e. g. , cloud or shadow. Time series interpolation at distinct reservoirs, for example using Amelia  [63] , can produce estimates of missing monthly reservoir areas, but levels of uncertainty are likely to be high over many reservoirs and years due to the large data gaps.
Policy Applications in Agricultural Landscapes In the Volta basin, applying our approach shows that the availability of small reservoir water is currently highly uneven across space and through the seasons. For example, dam density is higher in central Burkina Faso and northern Ghana, while some of the northern, western, and eastern regions of Burkina Faso, despite being among the driest in the Volta basin, are served by far fewer reservoirs. However, the extent of the gaps in GSW-derived data, which cover entre years for many reservoirs, limit its use for monitoring long-term trends in reservoir water availability. For some policy applications, this may not present a constraint, since long-term trends will be of less interest than analyses that show current conditions and short-term fluctuations, which can be derived from data post-2012 when underlying coverage is much higher in the GSW dataset for our West African site, and imagery from Landsat 8 OLI are available.
Gaps in the 32-year reservoir extent data derived from GSW-MH arise from masked pixels (e. g. , cloud or haze-covered) or a lack of imagery in the underlying Landsat collection  [16]. Additional gaps may arise from our automated area-extraction method where the reservoir point is outside the reservoir water extent. Missing data in monthly reservoir area estimates distorts individual and population reservoir storage means, maximums, and minimums, and limits the utility of the dataset for monitoring changes in reservoir surface areas through time. In addition to missing values in the GSW-MH dataset, a limitation of our method is that the connectedPixelCount algorithm in GEE used to extract reservoir extents can handle up to 1024 connected pixels, meaning that reservoirs that cover a larger area will be underestimated.
For Landsat data at 30 m resolution, this area limit is equivalent to 92. 16 ha. This is substantially larger than the 41. 2 ha upper size limit for small reservoirs, but creates a problem for the analysis of larger waterbodies. Reservoir estimates for these reservoirs can either be obtained by reducing the resolution (and therefore the number of pixels covered by a reservoir) on underlying Landsat imagery over larger reservoirs to enable use of the connectedPixelCount algorithm, or performing the analysis in a desktop GIS.
This knowledge may facilitate the implementation of safeguards to minimise water shortages and food losses, and can be used to inform decisions on future dam investments. We developed a semi-automated method for mapping reservoirs and their extents through time, and assessing uncertainty in Landsat-derived reservoir size estimates, which can be readily applied anywhere in the globe using freely available data and tools. We used our method to compare estimates of reservoir area that were derived from four approaches to classifying surface water from Landsat data. These include the approach that was used to create the Global Surface Water Monthly Water History (GSW) datasets  [16] , and surface water maps created in this study from Landsat 8 OLI imagery by computing the Normalised Difference Water Index (NDWI) and two variations of the Modified NDWI, which employ band 6 (MNDWI1) and band 7 (MNDWI2) as the short-wave infrared inputs, testing effects of classifying water across 11 thresholds.
Further research is required to check whether this result holds in other contexts, and for imagery that is collected by the Thematic Mappers and Enhanced Thematic Mapper onboard Landsat satellites 4, 5, and 7. Our study provides new information on the reliability of reservoir size and seasonality from GSW and other Landsat-based surface water maps, which is important given that these data are freely and globally available and use of information on reservoir resources for policy making without the knowledge of inherent uncertainties may have serious consequences. Whether or not the 19% increase in accuracy in reservoir area estimates obtained from using surface water maps generated by applying MNDWI1 to Landsat 8 OLI images rather than using the pre-prepared GSW data, depends on the end-use.
For agricultural planning and seasonal water resource management, we recommend that the use of GSW estimates be restricted to reservoirs with a maximum volume and monthly water loss of >49,759 m 3 (5. 1 ha), to avoid masked or false detection of water shortages. Further, Landsat-based approaches are often unable to detect any water in very small reservoirs, namely those <1. 5 ha using MNDWI1 and <2. 9 ha using GSW. New opportunities for the remote monitoring of small reservoirs are opening up with the availability of Sentinel-1 radar and high resolution (up to 10 m) Sentinel-2 optical data, which together should help to reduce spatial and seasonal data gaps, and improve the accuracy of derived reservoir area estimates. In the meantime, adopting integrated approaches to mapping small reservoirs remotely, such as manual digitising reservoir locations from high resolution imagery combined with automated reservoir extent extraction from Landsat imagery, is essential to avoid entirely omitting numerous water bodies.
Our study provides new information on the reliability of reservoir size and seasonality from GSW and other Landsat-based surface water maps, which is important given that these data are freely and globally available and use of information on reservoir resources for policy making without the knowledge of inherent uncertainties may have serious consequences. Whether or not the 19% increase in accuracy in reservoir area estimates obtained from using surface water maps generated by applying MNDWI1 to Landsat 8 OLI images rather than using the pre-prepared GSW data, depends on the end-use. For agricultural planning and seasonal water resource management, we recommend that the use of GSW estimates be restricted to reservoirs with a maximum volume and monthly water loss of >49,759 m 3 (5. 1 ha), to avoid masked or false detection of water shortages.
Further, Landsat-based approaches are often unable to detect any water in very small reservoirs, namely those <1. 5 ha using MNDWI1 and <2. 9 ha using GSW. New opportunities for the remote monitoring of small reservoirs are opening up with the availability of Sentinel-1 radar and high resolution (up to 10 m) Sentinel-2 optical data, which together should help to reduce spatial and seasonal data gaps, and improve the accuracy of derived reservoir area estimates. In the meantime, adopting integrated approaches to mapping small reservoirs remotely, such as manual digitising reservoir locations from high resolution imagery combined with automated reservoir extent extraction from Landsat imagery, is essential to avoid entirely omitting numerous water bodies.
Acknowledgments: This research was supported by a two-year CGIAR Water, Land and Ecosystems project called Targeting agricultural innovation and ecosystem services in the northern Volta basin (TAI). We would like to thank all donors who supported this research through their contributions to the  CGIAR Fund. Sarah Jones  is grateful to  Bioversity International and King's College London  for providing her with additional funding that helped take this research to completion. David Smedley would like to thank the  NERC DTP  for supporting his work through award  NE/L002485/1. We gratefully acknowledge  Simone Turner  and  Lambert Ngenzi  for their input to creating the validation dataset,  Elise Devine  for helping snap the dam point dataset, and  Bradley Luff  for technical support. Bioversity International covered the costs of open access publishing.
Abstract. We present a new water percolation routine added to the one-dimensional snowpack model Crocus as an alternative to the empirical bucket routine. This routine solves the Richards equation, which describes flow of water through unsaturated porous snow governed by capillary suction, gravity and hydraulic conductivity of the snow layers. We tested the Richards routine on two data sets, one recorded from an automatic weather station over the winter of 2013-2014 at Filefjell, Norway, and the other an idealized synthetic data set. Model results using the Richards routine generally lead to higher water contents in the snow layers. Snow layers often reached a point at which the ice crystals' surface area is completely covered by a thin film of water (the transition between pendular and funicular regimes), at which feedback from the snow metamorphism and compaction routines are expected to be nonlinear.
A dual-domain Richards-based model has begun explaining preferential flow paths, which can reproduce some of the ice layers present in the snowpack  (Wever et al. , 2016b). The Richards equation, when applied to snow, describes water percolation through a porous ice matrix considering the water retention curve and the hydraulic conductivity of snow layers. A one-dimensional Richards equation solver was recently added to the detailed snow model SNOWPACK  (Wever et al. , 2014 (Wever et al. , , 2015)). We added a similar physical water transport routine to the snowpack model Crocus. This paper will discuss the parameterization, sensitivity of the new routine and compare difference in water percolation with the bucket routine for two data sets.
The bucket approach To describe water percolation in snow, Crocus has historically used an empirically based routine, the so-called bucket routine that has been calibrated using long time series of lysimeter data  (Morin et al. , 2012)  and drainage experiments on the irreducible water content  (Coleou and Lesaffre, 1998). The bucket routine uses a holding capacity, defined by a percentage of the snow layer pore space. A snow layer's "bucket" is filled up with water when water is introduced via rain or melt. Once the bucket is full, overflow occurs to fill up the subsequent snow layer's bucket, restricting water motion in the downward direction. The "bucket size" or holding capacity has been defined as 5 % of a layer's pore spaces as default, although this can be adapted if needed  (Vionnet et al.
Implementation of the Richards routine in Crocus The Richards routine describes motion of water through an unsaturated porous matrix considering capillary-driven and gravity flow. Recent developments in parameterizing the conductivity of snow and snowpack water retention allow for the implementation of the Richards equation in layered snow pack models. In contrast to the bucket model, the Richards routine allows for upward motion of water if capillary pressure conditions are suitable. A Richards solver has recently been implemented in the SNOWPACK model  (Wever et al. , 2014). A comparison between the bucket percolation and Richards percolation in the SNOWPACK model showed that the Richards routine performed better than the bucket routine for a subday timescale when compared to lysimeter data  (Wever et al.